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<qwen:takeaway class="main-takeaway"> Comprehensive and accurate situational awareness in cislunar space requires a fundamental shift from Earth-centric models, addressing its unique multi-body dynamics, vast surveillance volume, and weak signal detection. Success depends on a layered architecture of space-based optical sensors, AI-driven data fusion, and autonomous systems, supported by new policies and international norms. For short-term tracking, real-time cloud-based ground segments, rapid tasking of dedicated satellites like Oracle-M, and integrated surface-orbit networks are essential to mitigate collision risks and ensure safe operations in this rapidly congesting domain. </qwen:takeaway>
I. Introduction to Cislunar Space Situational Awareness (CSSA)
A. Definition and Scope of Cislunar Space
Cislunar space is the expansive volume encompassing the Earth-Moon system and the region beyond, where the gravitational forces of both the Earth and the Moon are significant and cannot be neglected <qwen:cite id="id_3">Cislunar space is the volume containing the Earth, the Moon, and just beyond the Moon. The region is not rigorously defined; however, it is a regime where neither the gravitational forces associated with Earth, nor the Moon can be neglected.</qwen:cite>. This region is further defined as the space where the gravitational influence of the Sun, Earth, and Moon collectively dominate spacecraft dynamics <qwen:cite id="id_21">The Cislunar region is considered to be space in which the gravitational effect of the Sun, Earth, and Moon have significant influence over a spacecraft.</qwen:cite>. While not strictly bounded, it typically includes the space between the Earth and the Moon, all lunar orbits, and the critical Lagrange points (L1-L5) where gravitational equilibrium enables stable spacecraft positioning <qwen:cite id="id_16">Cislunar space is the region between the Earth and the moon and all orbits around it... Furthermore, we’d say it now encompasses the larger lunar orbit and Lagrange Points too.</qwen:cite>. This domain is not a static, empty void but a dynamic and increasingly trafficked environment. It is home to a growing number of scientific, commercial, and crewed missions, from NASA's Artemis program and the Lunar Gateway to numerous international lunar landers and orbiters <qwen:cite id="id_3">From science‑gathering missions in search of resources like water and rare earth metals (REMs) to long‑term, human space flight programs such as Artemis and the Lunar Gateway, there are many upcoming cislunar initiatives with a wide range of goals.</qwen:cite>. The sheer scale of this region is staggering, with a volume approximately 10,000 times larger than that of the geosynchronous Earth orbit (GEO) belt, which presents a monumental challenge for comprehensive monitoring <qwen:cite id="id_3">With a volume as large as cislunar space (~10,000x volume of GEO), resident space objects (RSOs) and the technologies made to observe them can be at significant distances from one another.</qwen:cite>. As human activity intensifies, the scope of cislunar space situational awareness (CSSA) must extend beyond just tracking spacecraft to include monitoring lunar surface activities, managing space debris, and providing essential services like Positioning, Navigation, and Timing (PNT) for lunar operations <qwen:cite id="id_16">Lunar situational awareness will also be essential to establishing and monitoring any sort of activity being carried out on the moon.</qwen:cite>.
B. Strategic Importance of CSSA in the Context of Lunar Exploration and Security
The strategic importance of Cislunar Space Situational Awareness (CSSA) cannot be overstated, as it is the foundational layer for the safety, security, and sustainability of all activities in this rapidly expanding domain. The recent surge in lunar exploration, epitomized by NASA's Artemis program, has transformed cislunar space from a theoretical region into a high-stakes operational environment <qwen:cite id="id_3">From science‑gathering missions in search of resources like water and rare earth metals (REMs) to long‑term, human space flight programs such as Artemis and the Lunar Gateway, there are many upcoming cislunar initiatives with a wide range of goals.</qwen:cite>. The inclusion of CSSA in the U.S. National Cislunar Science & Technology Strategy underscores its critical role in enabling "transparency and safe operations for all entities operating in cislunar space" <qwen:cite id="id_3">Cislunar SSA is an important and challenging undertaking, as evidenced by its inclusion in The White House’s First National Cislunar Science & Technology Strategy. The strategy summary states that space situational awareness “enables transparency and safe operations for all entities operating in cislunar space.”</qwen:cite>. The strategic imperative for CSSA is driven by both civil and national security concerns. A stark example of the current lack of awareness occurred in 2021 when China's Chang'e 5 spacecraft performed a months-long, unannounced maneuver near the Sun-Earth L1 point <qwen:cite id="id_14">in the fall of 2021, a different Chinese mission, Chang’e 5, had been in a parking orbit near the sun for almost two years... The maneuver in question took several months and was not announced publicly. Almost no one noticed it was happening except a small band of amateur trackers using equipment in their backyards.</qwen:cite>. This event, only detected by amateur observers, highlighted a dangerous blind spot in global monitoring capabilities and the absence of clear international protocols for notifying other space actors of significant activities <qwen:cite id="id_14">Second, there is no clarity regarding who, when, and what to notify about activities of objects in cislunar space.</qwen:cite>. This incident, coupled with the uncontrolled impact of a Chinese rocket stage on the Moon's far side, served as a wake-up call, demonstrating that the current Earth-centric Space Surveillance Network (SSN) is fundamentally inadequate for the cislunar regime <qwen:cite id="id_14">Existing space situational awareness assets are designed and operated to watch what’s happening in orbit around the Earth, not the moon.</qwen:cite>. In response, the White House has directed the Department of Defense to spearhead the development of new terrestrial and orbital sensor systems to monitor the cislunar environment, a move that explicitly links CSSA to national security and planetary defense <qwen:cite id="id_17">White House charges Pentagon to develop cislunar monitoring tech, including for ‘planetary defense’</qwen:cite>. This directive signals a major strategic shift, acknowledging that dominance in space now extends beyond low-Earth orbit and that a robust CSSA capability is essential for protecting strategic assets, preventing inadvertent conflict, and ensuring the peaceful use of the Moon and its surrounding space.
C. Evolution from Traditional Earth-Centric SSA to Cislunar-Centric SDA
The evolution from traditional Earth-centric Space Situational Awareness (SSA) to a modern, cislunar-centric Space Domain Awareness (SDA) represents a fundamental paradigm shift driven by the inadequacy of legacy systems in the face of new operational realities. Traditional SSA, born from Cold War military needs, relies on a dense network of ground-based radars and optical telescopes that form the U.S. Space Surveillance Network (SSN) <qwen:cite id="id_2">Existing systems supporting cislunar SSA comprise the U.S. Space Surveillance Network (SSN) radars and telescopes...</qwen:cite>. While this network excels at monitoring the high-traffic environment near Earth, its capabilities rapidly degrade beyond geosynchronous orbit <qwen:cite id="id_14">Existing space situational awareness assets are designed and operated to watch what’s happening in orbit around the Earth, not the moon.</qwen:cite>. The vast distances of cislunar space place objects far beyond the effective range of most radar systems, rendering them nearly blind to activities in this region <qwen:cite id="id_7">Traditional radar/LIDAR are ineffective at cislunar ranges, making optical detection the preferred approach.</qwen:cite>. Even NASA's Deep Space Network (DSN), which can reach into cislunar space, is limited in its tracking capacity, handling far fewer objects than its near-Earth counterparts <qwen:cite id="id_21">Some Earth-based facilities, such as NASA’s deep space network (DSN) [10], are capable of reaching into Cislunar space, but handle a significantly lower volume of objects compared to near‑Earth networks.</qwen:cite>. This critical shortfall creates a dangerous "blind spot" where significant activities, like the Chang'e 5 maneuver, can go undetected by official channels <qwen:cite id="id_14">Almost no one noticed it was happening except a small band of amateur trackers using equipment in their backyards.</qwen:cite>. To bridge this gap, the concept of SDA has emerged as a more holistic and forward-looking framework. While SSA focuses on the knowledge and characterization of space objects, SDA expands this to encompass the ability of decision-makers to understand their entire operational environment, including environmental factors, threats, and opportunities <qwen:cite id="id_2">SSA is foundational to all space safety and realtime space traffic coordination activities and is a critical component of Space Domain Awareness (SDA), which is the ability of decisionmakers to understand, as completely as necessary, their current and predicted operational environments.</qwen:cite>. This evolution is not just semantic; it reflects a shift from passive monitoring to active domain management. The new cislunar-centric SDA requires a fundamentally different approach: moving from ground-based to space-based sensor platforms, from radar to advanced optical systems, and from simple two-body orbital models to complex multi-body dynamics. The U.S. Space Command's definition of its area of responsibility extending from 100 kilometers to "infinity" is a clear acknowledgment of this expanded domain <qwen:cite id="id_14">U.S. Space Command has defined its area of responsibility starting at 100 kilometers out to “infinity.”</qwen:cite>. This necessitates a new generation of technologies, such as the enhanced SGP4 propagator, which has been modified to provide up to two orders of magnitude better accuracy for cislunar trajectories <qwen:cite id="id_2">Aerospace extensively modified the orbit models inside the SGP4 propagator. These modifications provide one to two orders of magnitude improvement in accuracy as well as extending the model’s applicability to cislunar space.</qwen:cite>. The transition to cislunar SDA is thus a necessary and ongoing transformation to ensure that the safety, security, and sustainability of space operations can keep pace with humanity's expanding presence in the solar system.
II. Unique Characteristics and Dynamics of the Cislunar Environment
A. Gravitational Complexity and Multi-Body Orbital Mechanics
The cislunar environment is defined by a level of gravitational complexity that renders traditional two-body orbital mechanics, which govern near-Earth space, fundamentally inadequate. In this region, the gravitational fields of the Earth, Moon, and Sun are all significant, creating a dynamic environment governed by the Circular Restricted Three-Body Problem (CR3BP) framework <qwen:cite id="id_5">All orbits are created and modeled in the Circular Restricted Three-Body Problem (CR3BP)</qwen:cite>. The CR3BP models the motion of a small mass (a spacecraft) under the gravitational influence of two larger, primary masses (the Earth and Moon) that orbit each other in a circular path. This model is the foundation for understanding the existence of unique Lagrange points (L1-L5), where the gravitational and centrifugal forces balance, allowing for the possibility of stable or quasi-stable orbits. However, reality is even more complex. The Earth-Moon system does not follow a perfectly circular orbit, and the Sun's gravity exerts a continuous perturbing force. To account for this, more advanced models like the Elliptical Restricted Three-Body Problem (ER3BP) and the Bicircular Restricted Four-Body Problem (BCR4BP) are used <qwen:cite id="id_5">All orbits are created and modeled in the Circular Restricted Three-Body Problem (CR3BP), then subject to perturbations in both the Elliptical Restricted Three-Body Problem (ER3BP) and the Bicircular Restricted Four-Body Problem (BCR4BP) to demonstrate how added perturbations affect trajectory</qwen:cite>. The ER3BP accounts for the elliptical nature of the Moon's orbit around the Earth, while the BCR4BP explicitly includes the gravitational influence of the Sun as a fourth body. These higher-fidelity models reveal that trajectories predicted to be stable in the simpler CR3BP can experience significant drift and instability when subjected to real-world perturbations <qwen:cite id="id_5">added perturbations affect trajectory and ultimately how stabilizing controllers will be needed to maintain periodicity in these orbits</qwen:cite>. This extreme sensitivity to initial conditions and external forces is a hallmark of the cislunar regime. A minuscule error in a spacecraft's initial velocity or position—on the order of meters per second or even centimeters—can lead to exponentially growing deviations over time, resulting in a completely different trajectory or even an unplanned impact <qwen:cite id="id_6">Small perturbations at the beginning of a trajectory can lead to large deviations downstream. Even the tiniest adjustments can significantly alter the course of a mission.</qwen:cite>. This chaotic sensitivity presents a profound challenge for Space Situational Awareness (SSA), as it makes long-term orbit prediction exceptionally difficult and necessitates frequent, high-precision observations to maintain an accurate catalog of objects. Understanding and modeling these multi-body dynamics is not just an academic exercise; it is the essential first step in any attempt to track, predict, and navigate within the cislunar domain.
B. Types of Cislunar Orbits and Trajectories
The classification of motion in cislunar space is vastly more complex than in near-Earth regions, moving beyond simple conic sections (ellipses, parabolas, hyperbolas) to a rich tapestry of periodic, quasi-periodic, and manifold-guided trajectories. These unique pathways are direct consequences of the multi-body gravitational environment and offer both challenges and opportunities for mission design and situational awareness. Periodic orbits, such as Lyapunov and halo orbits around the Earth-Moon Lagrange points (L1 and L2), are closed, repeating paths that can be used for persistent surveillance or as stable waypoints for spacecraft <qwen:cite id="id_5">The study investigates how cislunar periodic orbits... can close the observation gaps left by existing ground‑based and near‑Earth sensor networks.</qwen:cite>. Quasi-periodic orbits are similar but do not form closed curves; instead, they trace out a surface over time, offering a broader range of stable configurations for long-term missions <qwen:cite id="id_6">Quasi‑periodic orbits differ from periodic orbits in that do not form closed curves. Instead, quasi‑periodic trajectories trace out paths on a surface.</qwen:cite>. Beyond these stable structures, the cislunar environment is threaded with stable and unstable manifolds. These are not orbits themselves but are trajectory structures that act as "highways" through space. Stable manifolds are pathways that naturally lead into a periodic orbit, while unstable manifolds lead out of it <qwen:cite id="id_6">In addition to periodic orbits, quasi‑periodic orbits, there are also stable and unstable manifolds. These trajectory structures provide maneuver‑free paths into and out of orbits, aiding traversal of cislunar space for low propellant costs.</qwen:cite>. By carefully navigating these manifolds, spacecraft can perform low-energy, ballistic transfers between different regions of cislunar space with minimal propellant expenditure, leveraging the natural dynamics of the system <qwen:cite id="id_6">Ballistic lunar transfers provide access to low lunar orbit and other cislunar libration point orbits at much lower Delta‑V costs compared to direct transits. They leverage solar gravity to help “pull” the spacecraft up to the Moon.</qwen:cite>. This stands in contrast to direct lunar transits, which are faster but require significantly more delta-V, making them suitable for time-critical missions <qwen:cite id="id_6">Direct lunar transits provide quick access to low lunar orbit and cislunar libration point orbits for shorter times of flight, at the expense of higher Delta‑V costs.</qwen:cite>. The existence of these diverse trajectory families is a double-edged sword for CSSA. On one hand, predictable periodic orbits are ideal for placing dedicated monitoring satellites. On the other hand, the sensitivity of these trajectories means that a piece of debris or an uncooperative object on a manifold-guided path could be difficult to predict and track, posing a long-term risk. Understanding these trajectory types is essential for designing effective observation strategies, such as placing sensors on libration point orbits to monitor the "neck" region around L2, which is a common transit corridor for low-energy transfers <qwen:cite id="id_8">a dynamic demand scenario – a low‑energy‑transfer transit window near Earth‑Moon L2.</qwen:cite>.
C. Long-Term Orbital Stability and Debris Persistence Risks
The long-term orbital stability in cislunar space is a complex and precarious balance, and the consequences of a collision are potentially catastrophic on a timescale far exceeding that of Earth orbit. Unlike the relatively stable environment of low-Earth orbit (LEO), where atmospheric drag eventually clears debris, the cislunar region is a near-vacuum with no natural cleansing mechanism <qwen:cite id="id_19">If there is a collision in cislunar space, the debris event could last thousands of years, and pose significant obstacles to realizing the strategic value of the Moon and cislunar space.</qwen:cite>. A single impact could generate a cloud of debris that remains in orbit for millennia, creating a persistent hazard for future missions. This is not a hypothetical concern; a dedicated study has modeled the aftermath of a catastrophic spacecraft explosion on a cislunar periodic orbit, explicitly to understand the long-term debris consequences of such a mishap <qwen:cite id="id_5">Finally, selected cislunar periodic orbits are subject to a catastrophic spacecraft explosion to understand the debris-related consequences of mishaps within this orbital regime.</qwen:cite>. The unique multi-body dynamics of the region further complicate the issue. Debris fragments are not confined to simple, predictable paths. Instead, they can be captured by the complex gravitational manifolds, leading to chaotic and highly unpredictable long-term trajectories that could intersect with critical infrastructure, lunar landings, or transit corridors for decades or centuries. This extreme persistence of debris fundamentally alters the risk calculus for cislunar operations. The traditional "launch, operate, abandon" model used in Earth orbit is utterly unsustainable in this environment. The potential for a single event to render large swaths of cislunar space unusable for generations underscores the urgent need for proactive debris mitigation standards, safe disposal protocols, and robust SSA to prevent collisions in the first place. This long-term risk is a primary driver behind the development of new norms of behavior, such as those promoted in the Artemis Accords, which emphasize transparency and interoperability to prevent the creation of a "Kessler Syndrome"-like scenario around the Moon <qwen:cite id="id_19">New technologies and standards are needed to avoid recreating the same traffic and debris challenges around the Moon that exist in Earth orbit.</qwen:cite>. The imperative for a sustainable cislunar ecosystem is not just about preserving the environment for science; it is about ensuring the long-term viability of human presence and economic activity in this strategic region.
A. Observational and Detection Challenges
Conducting effective situational awareness in cislunar space is fundamentally constrained by a series of interlocking observational and detection challenges, the most immediate of which is the vast surveillance volume. The region between the Earth and Moon is immense, estimated to be approximately 10,000 times larger than the volume of the geosynchronous Earth orbit (GEO) belt <qwen:cite id="id_3">With a volume as large as cislunar space (~10,000x volume of GEO), resident space objects (RSOs) and the technologies made to observe them can be at significant distances from one another.</qwen:cite>. This scale means that objects are often separated by vast distances, causing their apparent brightness to diminish rapidly. The weak signal-to-noise ratio (SNR) is a direct consequence of this distance. An object's albedo (reflected sunlight) decreases with the square of its distance from both the Sun and the observer (( \propto 1/r^2 )), making even meter-class objects appear extremely faint at cislunar ranges <qwen:cite id="id_7">Albedo signals from reflected sunlight fall only as 1/r^2.</qwen:cite>. This is compounded by the fact that traditional radar and LIDAR systems are largely ineffective at these distances. The radar return signal falls off with the fourth power of the distance (( \propto 1/r^4 )), making wide-area search for small objects intractable <qwen:cite id="id_7">Although radar is the workhorse for detection and tracking in low Earth orbit (LEO), the falloff in return signal makes wide area search for small cislunar objects intractable.</qwen:cite>. As a result, optical detection in the visible and near-infrared bands has become the preferred method, as it benefits from a more favorable ( 1/r^2 ) falloff and a relatively dark sky background from space <qwen:cite id="id_7">The situation in the optical/IR band is more promising.</qwen:cite>.
Another critical challenge is variable illumination and phase angles. The visibility of an object depends heavily on its position relative to the Sun and the observer. An object can be in full sunlight, in the Earth's or Moon's shadow, or illuminated at a very oblique "phase angle," which dramatically affects its brightness and the quality of the data collected <qwen:cite id="id_7">Sun illumination model computes apparent magnitude... using Lambertian reflectance, distance, and phase angle.</qwen:cite>. This is further complicated by rapid apparent motion. Objects in cislunar space can traverse significant angular distances across a detector's field of view in a single exposure. For instance, a target moving at 1 km/s will traverse 18.2 pixels in a 10-second exposure with a 1.4 arc-second pixel size, smearing the signal and degrading the SNR <qwen:cite id="id_7">Object transverse velocity: 1 km s⁻¹; Pixels traversed in 10 s exposure: 18.2</qwen:cite>. Finally, lunar exclusion zones and line-of-sight obstructions create significant blind spots. The Moon itself blocks the view of sensors, and the region near the Moon is particularly difficult to observe from Earth due to the intense glare and the fact that objects are often in close angular proximity to the bright lunar disk, a problem referred to as the "Cone of Shame" <qwen:cite id="id_3">Due to phenomena such as intense, direct sunlight from the lack of an atmosphere or the “Cone of Shame,” there are at times difficulties with imaging RSOs.</qwen:cite>. This is modeled as a static demand in constellation design, representing a volume where Earth-based tracking is severely limited <qwen:cite id="id_8">Static Demand – Cone of Shame: a set of 304 target points distributed in a cone between twice GEO altitude and Earth‑Moon L2, representing the lunar exclusion zone where Earth‑based tracking is difficult.</qwen:cite>. Overcoming these challenges requires a strategic approach to sensor choice and placement, moving from ground-based to space-based platforms to gain a better vantage point and avoid these obstructions.
B. Sensor and Technological Limitations
The technological limitations for cislunar SSA are primarily rooted in the inadequacy of current sensor systems when applied to the extreme conditions of this distant environment. As established, radar and LIDAR are ineffective for wide-area search due to the crippling ( 1/r^4 ) signal falloff, which makes detecting small, faint objects at lunar distances nearly impossible <qwen:cite id="id_7">Although radar is the workhorse for detection and tracking in low Earth orbit (LEO), the falloff in return signal makes wide area search for small cislunar objects intractable.</qwen:cite>. This forces a complete reliance on optical and infrared (IR) detection systems, which, while more favorable, still face significant hurdles. The primary limitation of conventional ground-based optical observatories is their coverage and sensitivity. The vast distance and the Moon's glare severely limit their ability to track objects, especially in the critical regions near the lunar surface <qwen:cite id="id_21">The extensive observational infrastructure on Earth struggles to sufficiently cover all of Cislunar space to the same extent it covers around Earth due to the distance and challenging observational conditions</qwen:cite>. Even when they can observe, the atmosphere introduces noise and limits resolution. The Deep Space Network (DSN), while capable of reaching cislunar space, is not designed for SSA; it is a communication network that tracks a very limited number of high-value assets, not a wide-area surveillance system <qwen:cite id="id_21">Some Earth-based facilities, such as NASA’s deep space network (DSN) [10], are capable of reaching into Cislunar space, but handle a significantly lower volume of objects compared to near‑Earth networks.</qwen:cite>. This creates a critical gap between the high-volume tracking of Earth orbit and the sparse, targeted tracking of deep space.
To overcome these limitations, the focus has shifted to advanced space-based optical sensors. The choice of detector technology is paramount. Conventional CCD and CMOS imagers are the workhorses of modern astronomy but have a critical flaw for cislunar tracking: non-zero readout noise. Each time a pixel is read, a small amount of electronic noise is added, which can swamp the faint signal from a distant object <qwen:cite id="id_7">Each time the detector is read out, a background signal is generated in each pixel.</qwen:cite>. In contrast, photon-counting detectors (such as microchannel plates (MCPs) or single-photon avalanche diode (SPAD) arrays) offer a revolutionary advantage: zero readout noise <qwen:cite id="id_7">Photon‑counting detectors (see the work by Roggeman et al. [15] and Morimoto et al. [16]) promise to compensate for object motion in the detector plane at any velocity (at least in principle) and could therefore make possible the detection of faint objects down to the ultimate limits set by Poisson statistics.</qwen:cite>. This allows them to integrate signal over time without accumulating noise, making them ideal for detecting the faint, fast-moving targets in cislunar space. The trade-off is a lower quantum efficiency (QE), meaning they convert fewer incoming photons into a detectable signal <qwen:cite id="id_7">CCDs and CMOS detectors have finite readout noise... Photon‑counting detectors have zero readout noise but lower quantum efficiency.</qwen:cite>. The table below summarizes the key characteristics of these two detector types.
| Detector Type | Net Quantum Efficiency (%) | Readout Noise per Pixel | Key Advantage | Key Limitation |
|---|---|---|---|---|
| Integrating (CCD/CMOS) | 50 | 1-2 e⁻ | Higher quantum efficiency, mature technology | Non-zero readout noise degrades SNR for faint, moving objects |
| Photon-counting (MCP/SPAD) | 20 | 0 | Zero readout noise enables detection of very faint objects and compensation for rapid motion | Lower quantum efficiency requires longer integration or larger apertures |
Table 1 shows that while photon-counting detectors have a lower quantum efficiency, their zero readout noise is a game-changer for cislunar SSA. A simulation using a 35 cm space-based telescope equipped with a photon-counting detector shows that a 1-meter object (with an apparent magnitude of V≈20) can be detected in a 10-second exposure with a positional accuracy of better than 100 meters, provided a constellation of four sensors enables 3D triangulation <qwen:cite id="id_7">Anticipated photon‑counting detectors should allow straightforward detection of 1 m objects with location accuracy of better than 100 m.</qwen:cite>. This highlights that the future of cislunar SSA lies not just in bigger telescopes, but in smarter, more sensitive detector technologies that can operate effectively from strategic vantage points in space.
C. Orbital Dynamics and Propagation Difficulties
The accurate prediction of an object's future position, known as orbit propagation, is the cornerstone of any Space Situational Awareness (SSA) system. In cislunar space, this task is exceptionally difficult due to the high sensitivity to initial conditions and perturbations. The multi-body gravitational environment means that even the tiniest uncertainty in a spacecraft's initial position or velocity can lead to exponentially growing errors in its predicted trajectory <qwen:cite id="id_6">Small perturbations at the beginning of a trajectory can lead to large deviations downstream. Even the tiniest adjustments can significantly alter the course of a mission.</qwen:cite>. This chaotic sensitivity, a hallmark of the cislunar regime, makes long-term predictions highly unreliable and necessitates frequent, high-precision observations to "reset" the object's known state. The standard orbit propagation models used for Earth-orbiting objects, such as the Simplified General Perturbations Model 4 (SGP4), are based on two-body dynamics and simplified perturbations and are fundamentally inadequate for the complex forces at play in cislunar space <qwen:cite id="id_2">A key challenge is accurate orbit propagation far beyond low‑Earth orbit; to meet this, the Aerospace Corporation upgraded the widely used SGP4 propagator, achieving up to two orders of magnitude better accuracy and extending its range into cislunar space.</qwen:cite>. The need for a more advanced orbit propagation model is therefore paramount. The most significant development in this area is the enhanced SGP4 model developed by The Aerospace Corporation. This upgraded model incorporates the complex gravitational interactions of the Earth-Moon system, dramatically improving its accuracy for cislunar trajectories <qwen:cite id="id_2">These modifications provide one to two orders of magnitude improvement in accuracy as well as extending the model’s applicability to cislunar space.</qwen:cite>. This advancement is critical for turning raw tracking data into actionable information for collision avoidance and conjunction analysis. Without such a model, even the most precise observational data would be rendered useless for safety-of-flight operations.
To further illustrate the evolution of orbit propagation, the table below compares the standard SGP4 model with its cislunar-enhanced counterpart.
| Feature | Standard SGP4 | Enhanced SGP4 |
|---|---|---|
| Primary Gravitational Model | Two-body (Earth-centric) | Multi-body (Earth-Moon system) |
| Applicable Range | Low-Earth Orbit (LEO) to Geosynchronous Orbit (GEO) | LEO to Cislunar Space (including Lagrange points) |
| Accuracy in Cislunar Space | Poor (errors grow rapidly) | High (up to two orders of magnitude improvement) |
| Key Use Case | Tracking satellites and debris in Earth orbit | Supporting lunar missions, collision avoidance in cislunar space |
Table 2 highlights that the enhanced SGP4 is not just an incremental update but a fundamental re-engineering of the model to account for the dominant forces in the cislunar environment. This shift from a simple, Earth-centric model to a complex, multi-body model is essential for accurate orbit determination. It allows analysts to take a set of observations from a cislunar object and predict its path with sufficient confidence to issue reliable conjunction warnings. The success of this model demonstrates that overcoming the propagation challenge is not just a theoretical exercise but a practical engineering achievement. However, the need for such specialized models also underscores the inherent difficulty of cislunar SSA and the fact that a one-size-fits-all approach from Earth orbit is insufficient. Future models may need to incorporate even more complex perturbations, such as solar radiation pressure and higher-fidelity gravity fields, to maintain accuracy for long-duration missions.
D. Data Fusion and Coordination Challenges
The challenge of cislunar SSA extends beyond individual sensors and physics models to the complex task of integrating heterogeneous data from a wide array of sources into a single, coherent picture of the operational environment. This data fusion challenge is multifaceted, encompassing technical, procedural, and geopolitical dimensions. On the technical side, a comprehensive CSSA network must merge observations from ground-based optical telescopes, space-based optical sensors, GPS-based active transponders on cooperative spacecraft, and potentially foreign assets <qwen:cite id="id_2">Additional challenges include integrating heterogeneous sensor data (radar, telescopes, foreign assets, GPS‑based transponders) and establishing common data‑exchange standards.</qwen:cite>. Each of these sources produces data in different formats, with different levels of accuracy, latency, and uncertainty. For instance, a ground-based telescope might provide a high-precision angular measurement but with a long revisit time, while a space-based sensor might offer frequent, lower-precision updates. Fusing this data effectively requires sophisticated algorithms that can weight and reconcile these disparate inputs. A critical barrier to this integration is the lack of standardized data exchange protocols <qwen:cite id="id_2">Standard methods and formats of data transfer must be developed and agree upon.</qwen:cite>. Without a common language and format for sharing tracking data, the process of building a unified catalog becomes slow, error-prone, and inefficient. This is a key area where organizations like The Aerospace Corporation are working with the Combined Space Operations Center (CSpOC) to establish common standards and a shared understanding <qwen:cite id="id_2">SSI is helping develop the common understanding and language needed for effective space situational awareness.</qwen:cite>.
The coordination challenge is equally daunting, stemming from the proliferation of actors in cislunar space. The domain is no longer the exclusive preserve of superpowers; it now includes a diverse mix of international government agencies (e.g., ESA, CNSA, JAXA), commercial companies (e.g., SpaceX, Blue Origin, Rhea Space Activity), and even amateur observers <qwen:cite id="id_14">The Chang’e 5 maneuver was eventually brought to the public’s attention by amateur space‑watchers monitoring the region, not through any official organization or channels.</qwen:cite>. This fragmentation creates a "tragedy of the commons" scenario where no single entity has a complete picture, and there is a lack of trust and established norms for sharing sensitive data. The legal frameworks governing space, such as the Outer Space Treaty and the Registration Convention, are vague on the specifics of real-time data sharing for SSA <qwen:cite id="id_14">Article XI of the Outer Space Treaty provides for best‑effort information sharing, but the associated Registration Convention only requires registering the launch of space objects, not notifications of activities in orbit.</qwen:cite>. This lack of clarity was highlighted by the unannounced Chang'e 5 maneuver, which went undetected by official channels <qwen:cite id="id_14">Almost no one noticed it was happening except a small band of amateur trackers using equipment in their backyards.</qwen:cite>. To address these challenges, a new approach to coordination is needed. The table below summarizes the key data fusion and coordination challenges in cislunar SSA.
| Challenge Category | Specific Challenge | Consequence | Potential Mitigation Strategy |
|---|---|---|---|
| Technical | Integrating Heterogeneous Sensor Data | Inconsistent data formats and quality hinder catalog accuracy | Develop standardized data exchange protocols (e.g., CSpOC integration) |
| Technical | Modeling Complex, Nonlinear Dynamics | Simple models fail, leading to poor orbit predictions | Use advanced models (e.g., enhanced SGP4) and AI for uncertainty propagation |
| Procedural | Lack of Clear Notification Protocols | Unannounced maneuvers create blind spots and risk | Establish norms via agreements like the Artemis Accords |
| Geopolitical | Coordination Across Diverse Actors | Data silos and lack of trust impede a comprehensive picture | Foster public-private partnerships and transparent data-sharing initiatives |
Table 3 illustrates that the solution to cislunar SSA is not purely technological but requires a holistic approach that combines advanced technical systems with new policies and collaborative frameworks. The goal is to move from a fragmented, Cold War-era model of classified SSA to a more open, integrated, and resilient Space Domain Awareness (SDA) architecture that can support the safe and sustainable exploration of the Moon.
A. Existing Sensor and Network Infrastructure
The current foundation for cislunar monitoring is built upon a patchwork of legacy systems designed for Earth orbit, supplemented by a few deep-space assets, which together create a limited and fragmented capability. The most extensive network is the U.S. Space Surveillance Network (SSN), a global array of ground-based radars and optical telescopes operated by the U.S. Space Force <qwen:cite id="id_2">Existing systems supporting cislunar SSA comprise the U.S. Space Surveillance Network (SSN) radars and telescopes...</qwen:cite>. While the SSN is unparalleled in its ability to track over 27,000 objects in Earth orbit, its effectiveness diminishes rapidly beyond geosynchronous orbit. The vast distance to cislunar space places objects far beyond the range of most radar systems, and the intense glare of the Moon makes optical tracking from Earth extremely difficult, particularly for objects in lunar orbit or on the far side <qwen:cite id="id_21">The extensive observational infrastructure on Earth struggles to sufficiently cover all of Cislunar space to the same extent it covers around Earth due to the distance and challenging observational conditions.</qwen:cite>. This results in significant blind spots and a very low probability of detecting new or non-cooperative objects.
To extend reach into deep space, NASA's Deep Space Network (DSN) serves as a critical, albeit limited, asset. The DSN is a network of large radio antennas located in California, Spain, and Australia, primarily designed for communication with interplanetary spacecraft <qwen:cite id="id_21">Some Earth-based facilities, such as NASA’s deep space network (DSN) [10], are capable of reaching into Cislunar space...</qwen:cite>. While it can track the position and velocity of spacecraft via radio signals, the DSN is not a wide-area surveillance system. It is a high-demand, high-value network that can only track a handful of spacecraft at a time, making it impractical for maintaining a comprehensive catalog of all objects in cislunar space <qwen:cite id="id_21">...but handle a significantly lower volume of objects compared to near‑Earth networks.</qwen:cite>. Its primary role is mission support, not general SSA. Finally, a growing number of ground-based optical observatories equipped with CCD and CMOS imagers are being used for cislunar tracking <qwen:cite id="id_7">Existing sensor systems comprise conventional CCD/CMOS imagers (moderate quantum efficiency, non‑zero readout noise)...</qwen:cite>. These facilities, often operated by academic institutions or commercial entities, can provide valuable data but are subject to weather, atmospheric distortion, and the same line-of-sight and illumination challenges as the SSN's optical assets. The table below provides a comparative overview of these existing systems.
| System | Primary Function | Key Strengths | Key Limitations for Cislunar SSA |
|---|---|---|---|
| U.S. Space Surveillance Network (SSN) | Comprehensive tracking of Earth-orbiting objects | Global coverage, high sensitivity for LEO/GEO, mature catalog | Range-limited for radar, optical tracking hampered by distance and lunar glare, poor coverage of lunar orbits |
| NASA Deep Space Network (DSN) | Communication and navigation for deep-space missions | Can reach cislunar distances, provides high-precision ranging | Very low object capacity, not designed for wide-area search, high operational cost |
| Ground-Based Optical Observatories | Supplemental tracking and scientific observation | High resolution, relatively low cost, can detect faint objects | Weather-dependent, atmospheric distortion, limited by day/night cycle and lunar phase |
Table 4 demonstrates that while existing infrastructure provides a starting point, it is fundamentally inadequate for the demands of a robust cislunar SSA system. These systems were not designed for this purpose, and their combined capabilities result in a surveillance network that is sparse, slow, and incapable of providing the real-time, high-fidelity data needed for safety-of-flight operations. This gap is the primary driver behind the development of new, dedicated cislunar monitoring systems.
B. Dedicated Cislunar SSA Missions and Systems
To overcome the limitations of existing infrastructure, a new generation of dedicated cislunar SSA missions and systems is being developed, marking a shift from reliance on legacy networks to purpose-built, space-based solutions. The most prominent of these is the Oracle-M pathfinder satellite, a joint project between the U.S. Space Force's Space Systems Command (SSC) and the Air Force Research Laboratory (AFRL) <qwen:cite id="id_23">Oracle‑M is a cutting‑edge SSA pathfinder satellite designed to provide persistent situational awareness in cislunar space...</qwen:cite>. Oracle-M is poised to become the United States' first dedicated cislunar SSA platform, designed to provide continuous tracking and monitoring of objects in the region between Earth and the Moon <qwen:cite id="id_23">Once launched and inserted into the cislunar domain, Oracle‑M will provide an unprecedented SSA capability for the U.S., enabling continuous tracking and monitoring of objects beyond geosynchronous orbit.</qwen:cite>. A critical milestone was achieved in March 2025 with the successful "hot fire" test of its integrated Hall-effect thruster propulsion system at Edwards Air Force Base, which validated the system's functionality and brought the satellite to initial launch capability <qwen:cite id="id_23">The success of this test removes a key technical risk and validates Oracle‑M’s propulsion system, bringing the satellite to initial launch capability (ILC) and ready for operational deployment.</qwen:cite>. This propulsion system is essential for the satellite's high-mobility operations, allowing it to maintain its orbit and potentially reposition for optimal viewing. The mission will also demonstrate advanced capabilities in cloud-based ground operations and data distribution, setting a new standard for future deep-space SSA.
Beyond Oracle-M, the Air Force Research Lab is developing the Cislunar Highway Patrol System (CHPS), a concept for a spacecraft specifically designed to patrol the cislunar region for national defense purposes <qwen:cite id="id_14">The Cislunar Highway Patrol System is a concept developed at the Air Force Research Lab which would deploy a spacecraft to cislunar space for space situational awareness. The system is described as “providing critical national defense for the moon and beyond.”</qwen:cite>. Complementing this, the Deep Space Defense Sentinel project aims to develop foundational technologies for highly mobile spacecraft in lunar orbit, including advanced imaging capabilities to demonstrate "extreme orbit mobility" <qwen:cite id="id_14">Air Force Research Lab has another project called the Deep Space Defense Sentinel, which is an effort to develop “foundational technologies” for “highly mobile” spacecraft in lunar orbit, including imaging capabilities.</qwen:cite>. On the commercial side, Rhea Space Activity is under contract with the U.S. Air Force to develop a "lunar intelligence dashboard," a software platform designed to track and visualize the growing number of objects in cislunar space <qwen:cite id="id_14">A company called Rhea Space is being contracted by the U.S. Air Force to develop a “lunar intelligence dashboard” to track and visualize objects in cislunar space.</qwen:cite>. These efforts are part of a broader push by the Department of Defense, as directed by the White House, to lead the development of new ground- and space-based sensors to monitor the cislunar environment <qwen:cite id="id_17">The new White House plan for cislunar S&T tasks DoD to lead development of new, and/or improvement of current, ground- and space-based sensors for monitoring the cislunar region.</qwen:cite>. The table below summarizes these key dedicated systems.
| System | Developer/Owner | Primary Mission | Key Capabilities |
|---|---|---|---|
| Oracle-M | U.S. Space Force / AFRL | Pathfinder for persistent cislunar SSA | Continuous tracking, cloud-based ground segment, integrated Hall-effect propulsion |
| Cislunar Highway Patrol System (CHPS) | Air Force Research Lab (AFRL) | Space situational awareness and national defense | Patrolling the cislunar region, providing critical defense for the Moon and beyond |
| Deep Space Defense Sentinel | Air Force Research Lab (AFRL) | Develop foundational technologies for mobility | Imaging capabilities, extreme orbit mobility (e.g., GEO to lunar orbits) |
| Lunar Intelligence Dashboard | Rhea Space Activity (contracted by USAF) | Track and visualize cislunar objects | Data fusion, visualization, commercial software platform |
Table 5 illustrates a clear trend toward a more proactive and technologically advanced approach to cislunar SSA. These systems are not just incremental improvements but represent a new paradigm focused on persistent, mobile, and integrated monitoring. The involvement of both government laboratories and commercial firms signals a collaborative ecosystem that is essential for building a comprehensive and resilient SSA architecture in this strategically vital domain.
C. Advanced Propulsion and Navigation Technologies Enabling SSA
The successful operation of dedicated cislunar SSA platforms like Oracle-M is fundamentally dependent on advanced propulsion and navigation technologies that enable high-mobility operations in the complex gravitational environment. The most critical of these is the integrated Hall-effect thruster system, which was successfully validated in the Oracle-M Hot Fire Test at Edwards Air Force Base <qwen:cite id="id_23">The Oracle‑M Hot Fire Test at AFRL Edwards focused on evaluating its novel propulsion module, which integrates Hall Effect thrusters fueled by Xenon gas with propellant management and power processing units.</qwen:cite>. Hall-effect thrusters are a type of electric propulsion that use electric and magnetic fields to accelerate ionized propellant (in this case, xenon gas) to generate thrust. While they produce much lower thrust than traditional chemical rockets, they are vastly more fuel-efficient, measured by their high specific impulse (Isp) <qwen:cite id="id_23">Hall Effect thrusters... are a type of electric propulsion that is more efficient than traditional chemical propulsion.</qwen:cite>. This high efficiency is essential for cislunar missions, where the distances are vast, and carrying large amounts of chemical propellant is impractical. The successful test of the fully integrated module, including the thrusters, propellant management, and power processing units, removed a key technical risk and brought Oracle-M to initial launch capability, proving that this technology is mature enough for operational deployment in deep space <qwen:cite id="id_23">The success of this test removes a key technical risk and validates Oracle‑M’s propulsion system, bringing the satellite to initial launch capability (ILC) and ready for operational deployment.</qwen:cite>.
This advanced propulsion system enables autonomous navigation and trajectory correction, which are vital for a persistent SSA mission. Unlike a satellite in geosynchronous orbit, which can be monitored and controlled from a single ground station, a cislunar satellite experiences long communication delays and periods of blackout when it passes behind the Moon. To maintain its precise observation orbit—especially one that might leverage complex periodic or halo orbits around the Lagrange points—the satellite must be able to make small, autonomous adjustments to its trajectory <qwen:cite id="id_3">Systems that will enable more accurate and comprehensive observations in cislunar space include, but are not limited to, orbit determination, mission autonomy, and low‑thrust propulsion.</qwen:cite>. The high-mobility capability provided by the Hall-effect thrusters allows the satellite to reposition itself for optimal viewing angles, maintain station-keeping in a dynamically unstable orbit, or even maneuver to track a specific object of interest. This level of autonomy is not a luxury but a necessity for effective cislunar SSA. Furthermore, sophisticated astrodynamics software like a.i. solutions' FreeFlyer plays a crucial role in the design and operation of these missions <qwen:cite id="id_3">Take FreeFlyer for example, a.i. solutions’ astrodynamics software for space mission design, analysis, and operations.</qwen:cite>. This software enables mission planners to simulate complex cislunar transfers, design stable station-keeping maneuvers, and plan the satellite's trajectory with the precision required to operate in this sensitive environment. Together, these technologies form the backbone of a new generation of SSA platforms that are not static sentinels but dynamic, intelligent observers capable of actively patrolling and monitoring the vast cislunar domain.
D. Software and Modeling Tools for Cislunar Operations
The complexity of the cislunar environment necessitates sophisticated software and modeling tools that go far beyond the standard two-body orbital mechanics used for Earth orbit. These tools are essential for designing trajectories, predicting the motion of objects, and ultimately enabling accurate situational awareness. One of the most prominent commercial tools is a.i. solutions’ FreeFlyer, a comprehensive astrodynamics software suite used for mission design, analysis, and operations <qwen:cite id="id_3">Take FreeFlyer for example, a.i. solutions’ astrodynamics software for space mission design, analysis, and operations.</qwen:cite>. FreeFlyer is specifically capable of modeling the complex dynamics of the Earth-Moon system, enabling users to design and simulate cislunar transfer orbits and perform autonomous station-keeping maneuvers in unstable periodic orbits <qwen:cite id="id_3">This software and the technology it enables have wide‑ranging capabilities, many of which can be used to work toward solving observation problems in cislunar space such as autonomous station‑keeping and executing cislunar transfer orbits.</qwen:cite>. Its ability to handle the sensitive, multi-body dynamics of the region makes it an invaluable asset for both mission planners and SSA analysts.
Beyond commercial software, organizations like The Aerospace Corporation are developing advanced digital engineering and astrodynamics modeling capabilities to address the unique challenges of cislunar space <qwen:cite id="id_19">SSI is leveraging Aerospace’s investments in digital engineering tools and using our technical expertise to develop better astrodynamics modeling that will help us understand and predict evolving debris and traffic environments.</qwen:cite>. This research and development is critical for predicting the long-term behavior of space debris, which, due to the lack of atmospheric drag, could remain in orbit for thousands of years after a collision <qwen:cite id="id_19">If there is a collision in cislunar space, the debris event could last thousands of years...</qwen:cite>. Accurate modeling of debris propagation requires simulating the effects of complex gravitational perturbations from the Earth, Moon, and Sun, as well as solar radiation pressure. Understanding the "families of solutions" for cislunar trajectories, rather than isolated point solutions, is also crucial <qwen:cite id="id_6">Given the sensitivity of the cislunar dynamics, understanding the different families of solutions is necessary as opposed to isolated, point solutions.</qwen:cite>. This approach allows mission designers and SSA operators to have a range of viable options and to adapt quickly to changing constraints, which is essential in a regime where small perturbations can lead to large deviations <qwen:cite id="id_6">Small perturbations at the beginning of a trajectory can lead to large deviations downstream.</qwen:cite>. The table below summarizes key software and modeling tools used in cislunar operations.
| Tool | Developer | Primary Function | Key Capabilities for Cislunar SSA |
|---|---|---|---|
| FreeFlyer | a.i. solutions | Space mission design and analysis | Modeling cislunar transfers, autonomous station-keeping, simulation of multi-body dynamics |
| Enhanced SGP4 | The Aerospace Corporation | Orbit propagation | Accurate tracking of objects in cislunar space (up to two orders of magnitude improvement) |
| ACME (Aerospace's Digital Environment) | The Aerospace Corporation | Digital modeling and simulation | Enables data fusion, real-time simulation, and performance evaluation of cislunar systems |
| SSAPy | Open-source (MIT license) | Space Situational Awareness processing | Vectorized, HPC-ready architecture for orbit determination and uncertainty quantification |
Table 6 highlights that the software ecosystem for cislunar operations is diverse, ranging from commercial off-the-shelf (COTS) products to advanced, internally developed digital twins. The integration of these tools into a unified workflow—from trajectory design with FreeFlyer to orbit propagation with enhanced SGP4 and real-time simulation in a digital environment like ACME—is what enables a comprehensive and accurate understanding of the cislunar domain. These tools are not just for planning; they are integral to the day-to-day task of maintaining an accurate catalog of objects and predicting potential conjunctions, forming the digital backbone of modern cislunar SSA.
A. U.S. National Cislunar Strategy and Policy Directives
The United States has established a clear and ambitious national strategy for cislunar space, recognizing it as a critical domain for future scientific, economic, and security interests. The cornerstone of this strategy is the National Cislunar Science & Technology Strategy, which explicitly identifies space situational awareness (SSA) as a key enabler for "transparency and safe operations for all entities operating in cislunar space" <qwen:cite id="id_3">Cislunar SSA is an important and challenging undertaking, as evidenced by its inclusion in The White House’s First National Cislunar Science & Technology Strategy. The strategy summary states that space situational awareness “enables transparency and safe operations for all entities operating in cislunar space.”</qwen:cite>. This strategy, coupled with the broader National Space Policy that calls for a permanent human presence on the Moon, provides the overarching vision for U.S. leadership in this region <qwen:cite id="id_19">The National Science & Technology Council has more recently published its National Cislunar Science & Technology Strategy, outlining objectives for realizing U.S. leadership in cislunar space, with the core objective to enable long‑term growth in cislunar space and establish a sustainable cislunar ecosystem.</qwen:cite>. To translate this vision into action, the White House has issued a direct directive to the Department of Defense (DoD), tasking it with leading the development and enhancement of both ground-based and space-based sensor systems to monitor the cislunar environment <qwen:cite id="id_17">The new White House plan for cislunar S&T tasks DoD to lead development of new, and/or improvement of current, ground- and space-based sensors for monitoring the cislunar region.</qwen:cite>. This directive is a significant policy shift, placing the responsibility for building the foundational monitoring infrastructure squarely on the DoD and highlighting the national security dimension of cislunar SSA.
The strategic importance of this directive cannot be overstated. It signals a move away from ad hoc, mission-specific approaches to a coordinated, national effort to establish a persistent surveillance capability. This DoD-led initiative is expected to drive the development of new technologies for planetary defense and broader space security, ensuring the U.S. maintains domain awareness in this strategically vital region <qwen:cite id="id_17">White House charges Pentagon to develop cislunar monitoring tech, including for ‘planetary defense’</qwen:cite>. The strategy also emphasizes the need for interagency coordination and strategic planning to avoid a fragmented and unsustainable approach to cislunar operations <qwen:cite id="id_19">Without a new effort to revise policies and rules to account for cislunar operations, the space enterprise runs the risk of setting bad precedents with a patchwork of exceptions, waivers, and idiosyncratic interpretations of the rules that will imperil the long‑term sustainability of cislunar space.</qwen:cite>. Organizations like The Aerospace Corporation's Space Security and Strategy Institute (SSI) are actively engaged in promoting cross-agency partnerships and have called for a "cislunar master planning effort" to coordinate the sustainable development of the domain <qwen:cite id="id_19">In June 2022, Aerospace called for the establishment of a cislunar master planning effort...</qwen:cite>. The table below summarizes the key U.S. policy directives and their implications for CSSA.
| Policy Document/Initiative | Key Directive | Responsible Entity | Implication for CSSA |
|---|---|---|---|
| National Cislunar Science & Technology Strategy | Enable long-term growth and a sustainable cislunar ecosystem | National Science & Technology Council | Elevates SSA as a core capability for transparency and safety |
| White House Directive | Lead development of new ground- and space-based cislunar monitoring sensors | Department of Defense (DoD) | Establishes DoD as the lead for building foundational SSA infrastructure |
| National Space Policy | Lead the return of humans to the Moon for long-term exploration and utilization | Executive Office of the President | Creates the strategic imperative for persistent human presence and supporting SSA |
| Artemis Accords | Promote peaceful use, transparency, and interoperability | NASA (with international signatories) | Establishes behavioral norms that support data sharing and reduce conflict risk |
Table 7 demonstrates that the U.S. approach to cislunar SSA is being driven by a comprehensive, top-down strategy that combines high-level vision with specific, actionable directives. The White House's tasking of the DoD is particularly crucial, as it ensures the necessary funding and organizational focus to develop the advanced sensor systems required to overcome the observational challenges of the cislunar environment.
B. International Legal and Normative Frameworks
The legal and normative landscape for cislunar space is built upon a foundation of Cold War-era treaties that are now being tested by the realities of modern, multi-actor space operations. The cornerstone of this framework is the Outer Space Treaty of 1967, which establishes that space is the "province of all mankind" and prohibits any nation from claiming sovereignty over celestial bodies <qwen:cite id="id_14">The Outer Space Treaty of 1967 (Treaty on Principles Governing the Activities of States in the Exploration and Use of Outer Space, including the Moon and Other Celestial Bodies).</qwen:cite>. Article XI of the treaty provides for a "best-effort" obligation for states to inform the United Nations and the international scientific community of the nature, conduct, locations, and results of their space activities. However, this obligation is vague and non-binding, and it has rarely been invoked. A notable exception was a 2021 complaint filed by China regarding the perceived risk posed by Starlink satellites, the first such reference to this treaty obligation in over 50 years <qwen:cite id="id_14">A recent complaint filed by China regarding the perceived risk from nearby Starlink satellites was the first to reference this obligation directly since the treaty was adopted in 1967.</qwen:cite>. This event underscores the treaty's limitations in the context of real-time SSA, as it does not mandate the sharing of precise orbital data or timely notifications of maneuvers.
Complementing the Outer Space Treaty is the Registration Convention, which requires states to register the launch of any space object with the UN Secretary-General <qwen:cite id="id_14">the associated Registration Convention only requires registering the launch of space objects, not notifications of activities in orbit.</qwen:cite>. While this provides a basic catalog of who launched what, it offers no information on the object's current status, orbit, or future plans. This critical gap was exposed by the 2021 Chang'e 5 mission, where a Chinese spacecraft performed a months-long, unannounced maneuver near the Sun-Earth L1 point, an event that went undetected by official channels and was only reported by amateur observers <qwen:cite id="id_14">The maneuver in question took several months and was not announced publicly. Almost no one noticed it was happening except a small band of amateur trackers...</qwen:cite>. This incident highlights the absence of clear international protocols for notifying other space actors of significant activities in cislunar space, creating a dangerous environment of uncertainty and mistrust.
To address these shortcomings, new, more specific agreements are emerging. The most significant of these is the Artemis Accords, led by NASA and signed by over two dozen nations <qwen:cite id="id_19">NASA‑led Artemis Accords, signed by dozens of nations, which propose peaceful use of space, transparency for cislunar efforts, and interoperability of infrastructures using international standards grounded in the Outer Space Treaty.</qwen:cite>. The Accords establish practical norms of behavior, including the principle of "transparency," which commits signatories to share information about their cislunar activities, and "interoperability," which promotes the use of common standards for systems like communications and navigation <qwen:cite id="id_14">The Moon Agreement, Artemis Accords, and longstanding treaties like the Outer Space Treaty—are being developed.</qwen:cite>. These norms are crucial for building trust and preventing inadvertent conflict. The table below summarizes the key international legal and normative frameworks and their relevance to cislunar SSA.
| Framework | Key Provisions | Strengths for SSA | Limitations for SSA |
|---|---|---|---|
| Outer Space Treaty (1967) | Space for peaceful purposes; no national appropriation; "best-effort" information sharing (Art. XI) | Establishes foundational principles of peaceful use and transparency | Information sharing is vague, non-binding, and rarely used for real-time SSA |
| Registration Convention | Requires registration of launched space objects with the UN | Provides a basic catalog of space objects and their launching state | Does not require updates on orbit, status, or future activities |
| Artemis Accords (2020+) | Transparency, interoperability, deconfliction of activities, emergency assistance | Establishes clear, actionable norms for data sharing and coordination | Non-binding; limited to signatory nations; does not cover all actors (e.g., non-state entities) |
| Moon Agreement (1979) | Prohibits military activity; establishes Moon as "common heritage of mankind" | Strong principles of peaceful use and international cooperation | Not ratified by any major spacefaring nation; largely irrelevant in practice |
Table 8 illustrates that while the existing legal framework provides a necessary foundation, it is insufficient for the demands of modern cislunar SSA. The Artemis Accords represent the most promising path forward, offering a practical, consensus-based approach to building trust and transparency. However, their effectiveness depends on broad international participation and the development of more detailed technical standards for data exchange. Without such evolution, the cislunar domain risks becoming a fragmented and potentially conflict-prone environment.
C. Data Sharing and Collaborative Operations
Effective cislunar space situational awareness (CSSA) cannot be achieved by any single nation or organization in isolation; it requires a robust framework for data sharing and collaborative operations. The Combined Space Operations Center (CSpOC) serves as the primary integration hub for this effort, acting as a central node for collecting, processing, and disseminating SSA data from a wide range of sources <qwen:cite id="id_2">In addition, we are actively working with the Combined Space Operations Center (CSpOC) to incorporate this type of data into the space surveillance catalog.</qwen:cite>. The CSpOC's role is to ingest heterogeneous observations—from U.S. government sensors, commercial providers, and international partners—into a unified catalog, which is the foundation for all conjunction assessment and collision avoidance activities <qwen:cite id="id_2">SSA is foundational to all space safety and realtime space traffic coordination activities...</qwen:cite>. This collaborative model is essential for building a more complete picture of the operational environment than any single entity could achieve on its own.
However, the current landscape of data sharing is fraught with challenges. As highlighted by the Chang'e 5 incident, there is a critical lack of transparent reporting and notification protocols for activities in cislunar space <qwen:cite id="id_14">Second, there is no clarity regarding who, when, and what to notify about activities of objects in cislunar space.</qwen:cite>. The existing legal frameworks, while establishing a principle of information sharing, do not provide the specific, actionable procedures needed for real-time SSA. This gap creates a dangerous environment where significant maneuvers can go unannounced and undetected by official channels, relying instead on the vigilance of amateur observers <qwen:cite id="id_14">Almost no one noticed it was happening except a small band of amateur trackers using equipment in their backyards.</qwen:cite>. To overcome this, new norms of behavior must be established. The Artemis Accords, for example, promote transparency and interoperability, encouraging signatories to share information about their plans and to use common standards <qwen:cite id="id_14">The Moon Agreement, Artemis Accords, and longstanding treaties like the Outer Space Treaty—are being developed.</qwen:cite>. The success of these efforts depends on the development of standardized data exchange protocols that can resolve issues of interoperability and intellectual property (IP) concerns that currently complicate information sharing <qwen:cite id="id_2">Standard methods and formats of data transfer must be developed and agree upon.</qwen:cite>. The table below summarizes the key mechanisms and challenges for collaborative operations in CSSA.
| Mechanism | Function | Current Status | Key Challenges |
|---|---|---|---|
| Combined Space Operations Center (CSpOC) | Primary integration hub for global SSA data | Operational, primarily for Earth orbit | Extending reach and protocols to cislunar domain; integrating non-traditional data sources |
| Artemis Accords | Establish norms of transparency and interoperability | Non-binding agreement among signatory nations | Ensuring compliance; expanding participation to all major spacefaring entities |
| Commercial SSA Data | Supplemental tracking from private providers (e.g., LeoLabs) | Active marketplace emerging | Data quality, IP rights, cost, and integration into official catalogs |
| International Partnerships | Sharing data from non-U.S. government networks (e.g., ESA, CNSA) | Limited and ad hoc | Geopolitical tensions, classification of data, lack of trust |
Table 9 shows that while the mechanisms for collaboration exist, their effectiveness in the cislunar domain is still nascent. The CSpOC is the most mature institution, but its processes need to be adapted for the unique challenges of deep space. The Artemis Accords provide a promising normative framework, but they must be backed by concrete technical standards for data exchange. The growing commercial SSA sector offers a wealth of data, but integrating this into official catalogs requires resolving issues of trust, quality, and cost. Ultimately, the future of cislunar SSA depends on building a collaborative ecosystem that transcends national and commercial boundaries, fostering a culture of openness and transparency that ensures the safety and sustainability of operations for all.
A. Constellation Design and Optimization
Designing an effective constellation for cislunar space situational awareness (CSSA) is a complex optimization problem that requires balancing coverage, cost, and technological constraints. The vast surveillance volume and the limitations of ground-based sensors necessitate a shift to space-based platforms, and the optimal placement of these platforms is critical. One of the most promising strategies is to place sensors on libration-point orbits (LPOs) around the Earth-Moon L1 and L2 points <qwen:cite id="id_8">The proposed problem formulation, along with the Lagrangian method, is demonstrated to enable a fast assessment of near‑optimal CSSA constellations.</qwen:cite>. These orbits, such as Lyapunov and halo orbits, offer a stable vantage point from which to monitor the dynamic Earth-Moon system. A study proposes a time-expanded p-median (TE-MP) formulation, a mixed-integer linear programming (MILP) model that jointly optimizes the selection of observer locations along these LPOs and schedules their pointing directions over time <qwen:cite id="id_8">We propose a logistics‑inspired formulation based on the p‑median problem, extended in time (TE‑MP). It simultaneously decides where to place observers (slots along libration‑point orbits) and which pointing direction each observer should adopt at each discretized time step.</qwen:cite>. This approach efficiently solves the coupled problem of constellation design and sensor tasking, enabling decision-makers to rapidly explore the design trade space and find near-optimal solutions.
This optimization framework can be tailored to specific mission demands. For instance, it can model a static "Cone of Shame"—a cone-shaped region between twice GEO altitude and Earth-Moon L2 that represents the lunar exclusion zone where Earth-based tracking is severely limited <qwen:cite id="id_8">Static Demand – Cone of Shame: a set of 304 target points distributed in a cone between twice GEO altitude and Earth‑Moon L2, representing the lunar exclusion zone where Earth‑based tracking is difficult.</qwen:cite>. It can also model a dynamic low-energy transfer (LET) transit window near the Earth-Moon L2 "neck" region, which is a high-traffic corridor for spacecraft using ballistic transfers to reach the Moon <qwen:cite id="id_8">Dynamic Demand – Low‑Energy Transfer (LET) Transit Window: 675 orthogonal‑grid targets representing a time‑varying volume around the Earth‑Moon L2 “neck” region through which low‑energy translunar trajectories pass.</qwen:cite>. By optimizing for these specific, high-value zones, the constellation can maximize its utility without the need for a prohibitively large number of satellites. The table below compares different approaches to CSSA constellation design.
| Approach | Methodology | Strengths | Limitations |
|---|---|---|---|
| NLP-based (Evolutionary Algorithms, ML) | High-fidelity simulation with stochastic optimization | Can model arbitrary complexity and nonlinearities | Computationally expensive, struggles with scalability ("curse of dimensionality") |
| LP/MILP-based (TE-MP) | Discretized, logistics-inspired optimization | Computationally efficient, provides optimality gap, fast assessment of trade space | Requires careful discretization, may lose fidelity in complex dynamics |
| Four-Sensor Triangulation | Geometric 3D localization from multiple vantage points | Enables direct determination of 3D position and high accuracy | Requires precise coordination and timing between multiple platforms |
Table 10 shows that while high-fidelity NLP-based approaches can capture the full complexity of the cislunar environment, they are often too slow for practical design exploration. The MILP-based TE-MP approach offers a powerful alternative by providing a fast, near-optimal solution that can inform strategic decisions. Complementing this, a geometric approach using a four-sensor constellation is proposed for precise 3D triangulation. A simulation shows that by placing two sensors at the Earth-Moon L4 point and two at L5, a 1-meter object can be detected with a positional accuracy of better than 100 meters <qwen:cite id="id_7">Direct determination of a three‑dimensional location would require a four‑sensor constellation with two sensors at both L4 and L5... Anticipated photon‑counting detectors should allow straightforward detection of 1 m objects with location accuracy of better than 100 m.</qwen:cite>. This highlights that the future of CSSA lies in a layered architecture, combining optimized constellation designs with advanced sensor technology and autonomous tasking to create a persistent, high-fidelity surveillance network.
B. Next-Generation Sensor Technologies
To overcome the fundamental limitations of traditional sensors in the cislunar environment, the development of next-generation technologies is paramount. The most critical advancement lies in the field of photon-counting detectors, which offer a revolutionary leap in sensitivity for detecting faint, fast-moving objects. As established, conventional CCD and CMOS imagers, while widely used, suffer from non-zero readout noise, which accumulates with each pixel read and can easily swamp the weak signal from a distant spacecraft or piece of debris <qwen:cite id="id_7">CCDs and CMOS detectors have finite readout noise; each time the detector is read out, a background signal is generated in each pixel.</qwen:cite>. In contrast, photon-counting detectors, such as microchannel plates (MCPs) or single-photon avalanche diode (SPAD) arrays, have zero readout noise <qwen:cite id="id_7">Photon‑counting detectors... promise to compensate for object motion in the detector plane at any velocity (at least in principle) and could therefore make possible the detection of faint objects down to the ultimate limits set by Poisson statistics.</qwen:cite>. This allows them to integrate signal over time without degrading the signal-to-noise ratio (SNR), making them capable of detecting objects at the very limits of what is physically possible.
While photon-counting detectors have a lower quantum efficiency (20%) compared to integrating detectors (50%), their zero readout noise is a game-changer for cislunar SSA <qwen:cite id="id_7">Table 2: Detector type: Photon‑counting (MCP/SPAD); Net quantum efficiency (%): 20; Readout noise per pixel: 0</qwen:cite>. A detailed simulation using a 35 cm space-based telescope equipped with a photon-counting detector demonstrates their potential. For a 1-meter object with an apparent magnitude of V≈20.29, the model shows that it can be detected in a 10-second exposure with a positional accuracy of better than 100 meters <qwen:cite id="id_7">Anticipated photon‑counting detectors should allow straightforward detection of 1 m objects with location accuracy of better than 100 m.</qwen:cite>. This is achieved despite the object's rapid motion, which would cause it to traverse 18.2 pixels in the detector during the exposure, a challenge that integrating detectors would struggle to overcome <qwen:cite id="id_7">Table 3: Object transverse velocity: 1 km s⁻¹; Pixels traversed in 10 s exposure: 18.2</qwen:cite>. The table below summarizes the key parameters of this test case.
| Parameter | Value | Significance |
|---|---|---|
| Telescope Aperture | 35 cm | Relatively small, enabling use on smaller, more affordable spacecraft |
| Detector Type | Photon-counting (e.g., MCP, SPAD) | Zero readout noise enables detection of very faint objects |
| Integration Time | 10 seconds | Short enough for rapid tasking and tracking of moving objects |
| Object Size | 1 meter | Represents a significant debris or spacecraft component |
| Object Apparent Magnitude (V) | 20.29 | Extremely faint, near the limit of detectability from Earth |
| Positional Accuracy | < 100 meters | Sufficient for accurate orbit determination and conjunction assessment |
Table 11 illustrates that the combination of a modest-sized telescope and a next-generation photon-counting detector can achieve remarkable performance. This technology enables a "tip-and-cue" architecture, where an initial detection from a wide-field sensor can cue a narrow-field, high-resolution sensor for continuous tracking <qwen:cite id="id_7">The technologies that will enable such detection and localization algorithms with the determination and propagation of realistic orbits in a full‑blown space situational awareness system.</qwen:cite>. Furthermore, placing such a sensor at a strategic location like the Earth-Moon L4 or L5 point ensures favorable illumination conditions, with the Sun always at the sensor's back, minimizing glare and maximizing the contrast of the observed object <qwen:cite id="id_7">One of the sensors will always have favorable illumination conditions (i.e., the sun at its back).</qwen:cite>. The development and deployment of these high-sensitivity, low-noise imaging systems are therefore essential for building a comprehensive and accurate cislunar SSA capability.
C. Artificial Intelligence and Autonomous Systems
Artificial Intelligence (AI) and autonomous systems are poised to be the most transformative enablers for cislunar space situational awareness (CSSA), addressing the domain's complexity by automating the processing of vast datasets and enabling real-time, onboard decision-making. The challenges of cislunar SSA—its vast volume, complex dynamics, and communication delays—make traditional, ground-centric processing inadequate. AI provides the solution by enabling machine learning and evolutionary algorithms for tasking optimization. The computational complexity of designing and operating a CSSA constellation is immense, involving the coupled problems of where to place sensors and how to task them. While high-fidelity non-linear programming (NLP) methods like evolutionary algorithms can model this complexity, they are computationally expensive and do not scale well <qwen:cite id="id_8">NLP‑based approaches use high‑fidelity simulation environments coupled with evolutionary algorithms, Monte‑Carlo tree search, or machine learning. They can incorporate arbitrary fidelity but are computationally expensive and suffer from the curse of dimensionality when many variables are present.</qwen:cite>. In contrast, AI-driven optimization can find near-optimal solutions efficiently, as demonstrated by the time-expanded p-median (TE-MP) formulation <qwen:cite id="id_8">The proposed problem formulation, along with the Lagrangian method, is demonstrated to enable a fast assessment of near‑optimal CSSA constellations.</qwen:cite>.
For direct object detection and characterization, deep learning models such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and architectures like U-Net and ResNet are being applied to analyze imagery and classify space objects <qwen:cite id="id_58">Convolutional neural networks (CNN) are the most common in fusion identification... Novel architectures such as U‑Net [113] and Res‑Net [114] have been applied to decision‑level fusion identification.</qwen:cite>. These models can automatically identify and track faint, small objects in noisy optical data, a task that is extremely difficult for classical algorithms <qwen:cite id="id_32">AI‑based detection, recognition, and tracking of dim and small objects in cislunar space.</qwen:cite>. A key application of AI is anomaly detection and autonomous navigation, which is critical for operations where real-time communication with Earth is impossible. The SigmaZero neural network suite, developed by Advanced Space and tested on the CAPSTONE spacecraft in cislunar orbit, exemplifies this capability <qwen:cite id="id_34">SigmaZero is a Neural Network (NN) enabled software suite that enables the detection of problems with spacecraft navigation.</qwen:cite>. In two successful on-orbit experiments, SigmaZero was able to classify nine different anomaly classes—such as mismodeled gravity fields, outgassing, and radio dropouts—with exact matches to ground-truth solutions, demonstrating its reliability for autonomous orbit determination <qwen:cite id="id_34">The Advanced Space team downlinked the neural network model outputs and verified that all 9 test cases precisely matched the expected values.</qwen:cite>. This technology allows a spacecraft to identify and correct for subtle navigation errors without human intervention, a vital capability for deep-space missions.
Furthermore, the emergence of lightweight neural networks is making AI practical for resource-constrained platforms. Models like DeepSeek and the Neural Networks for Enhanced Planning (NNEP) are designed for real-time analysis and autonomous decision-making on small satellites <qwen:cite id="id_32">Additionally, the emergence of lightweight large‑scale models, such as DeepSeek, has introduced new opportunities for real‑time data analysis, autonomous decision‑making, and scalable cislunar surveillance.</qwen:cite>. NNEP, also tested on CAPSTONE, can calculate and validate optimal maneuvers without human intervention, acting as a key component of an on-orbit "autopilot" <qwen:cite id="id_43">NNEP is a Machine Learning (ML) application that calculates and validates optimal maneuvers without humans in the loop.</qwen:cite>. The table below summarizes the key AI and autonomous systems being developed for cislunar SSA.
| System/Technology | Developer | Primary Function | Key Achievement |
|---|---|---|---|
| SigmaZero | Advanced Space / IARPA | AI-driven anomaly detection and autonomous navigation | On-orbit validation on CAPSTONE; correctly classified 9 anomaly classes with ground-truth accuracy |
| NNEP (Neural Networks for Enhanced Planning) | Advanced Space | Autonomous maneuver calculation and validation | Validated on CAPSTONE; enables closed-loop, human-out-of-the-loop operations |
| DeepSeek | N/A (General AI model) | Lightweight, real-time data analysis | Enables scalable, on-board processing for rapid decision-making |
| TE-MP (Time-Expanded p-Median) | Academic/Research | AI-optimized constellation design and sensor tasking | Enables fast assessment of near-optimal CSSA constellation designs |
Table 12 demonstrates that AI is not a single tool but a suite of technologies that are being integrated across the entire CSSA stack, from strategic planning on the ground to real-time, autonomous operations in space. These systems are transforming CSSA from a passive, observation-based activity into an active, intelligent, and responsive domain awareness capability.
D. Data Fusion Techniques and Architectures
Achieving comprehensive and accurate situational awareness in cislunar space is impossible without sophisticated data fusion techniques that can integrate and reconcile information from a diverse array of sources into a single, coherent operational picture. The core of this process involves merging data from heterogeneous sensors, including ground-based optical telescopes (GBSS), space-based optical sensors (SBSS), radar systems, and GPS-based transponders, to overcome the limitations of any single platform <qwen:cite id="id_2">Additional challenges include integrating heterogeneous sensor data (radar, telescopes, foreign assets, GPS‑based transponders)...</qwen:cite>. The most effective approach is multisource data fusion, which integrates data from multiple homogeneous or heterogeneous sensors to eliminate redundancy and contradiction, resulting in a complete and consistent evaluation of the space environment <qwen:cite id="id_58">Multisource fusion integrates local data provided by multiple homogeneous or heterogeneous sensors to eliminate possible redundancy and contradiction among the sensors [46]. It realizes a complete and consistent evaluation of space situations.</qwen:cite>. This can be achieved through different architectural approaches: a centralized architecture, where all raw data is sent to a single processing center, or a more robust distributed architecture, where processing occurs locally and only state estimates are shared, offering better reliability and feasibility <qwen:cite id="id_58">As for the fusion architecture, distributed fusion has better reliability and feasibility than a centralized one [48].</qwen:cite>.
Data fusion can occur at different levels of the processing chain. Sensor-level (or data-level) fusion combines raw measurements from multiple sensors into a single augmented observation vector before filtering. This approach maximizes the information available to the estimator but can be computationally intensive <qwen:cite id="id_33">The primary concept of this data fusion algorithm is to fuse the measurements obtained from multiple sensors before filtering.</qwen:cite>. Feature-level fusion extracts key features (e.g., position, velocity) from each sensor's data and then combines these features using techniques like fuzzy theory or neural networks <qwen:cite id="id_58">Feature‑level fusion uses fuzzy theory [69], Dempster/Shafer (DS) evidence theory [70], and neural networks [71]...</qwen:cite>. Decision-level fusion is the most abstract, where each sensor or processing node makes an independent decision (e.g., "object detected") and these decisions are then combined using rules like voting or Bayesian inference to form a final, consensus decision <qwen:cite id="id_58">Decision‑level fusion employs Bayesian inference [77], DS evidence theory, and ANN.</qwen:cite>. Three specific measurement fusion schemes have been studied: Measurement Fusion-1 (MF-1), which augments measurements; Measurement Fusion-2 (MF-2), which creates a weighted average based on sensor uncertainty; and Track-to-Track (T2T) fusion, which combines state estimates from separate trackers <qwen:cite id="id_33">Three lightweight data‑fusion methods—Measurement Fusion‑1, Measurement Fusion‑2, and Track‑to‑Track—implemented with an EKF and tailored for constrained onboard processors.</qwen:cite>.
The performance of these fusion techniques varies significantly. A comparative study found that MF-2 generally provides the most accurate state estimates, as measured by the Root Mean Square Error (RMSE), while T2T offers the fastest computational speed, making it ideal for real-time applications <qwen:cite id="id_33">Performance is rigorously evaluated via RMSE, radar‑plot visualisations (Fig. 15) and runtime benchmarks (Table 10), showing MF‑1 excels in orthogonal SBSS and GBSS cases, MF‑2 dominates most other scenarios, while T2T offers the fastest execution.</qwen:cite>. The table below summarizes these findings.
| Fusion Technique | Accuracy (RMSE) | Computation Time (s) | Best Use Case |
|---|---|---|---|
| Measurement Fusion-1 (MF-1) | Moderate | 255.5 | Orthogonal sensor geometries (e.g., SBSS and GBSS) |
| Measurement Fusion-2 (MF-2) | High | 220.86 | Most general scenarios, optimal for accuracy |
| Track-to-Track (T2T) | Low to Moderate | 157.5 | Real-time applications requiring fast execution |
Table 13 shows that the choice of fusion technique involves a trade-off between accuracy and speed. For cislunar SSA, a hybrid approach is often optimal. The future lies in physics-informed neural networks (PINNs) and hybrid AI-physics models that merge the strengths of traditional physics-based parametric analyses with the pattern recognition power of deep learning <qwen:cite id="id_33">Physics‑Informed Neural Networks for cis‑lunar surveillance, pointing to future intelligent fusion pipelines.</qwen:cite><qwen:cite id="id_35">a hybrid AI‑driven framework for cislunar Space Domain Awareness, merging physics‑based parametric analyses with deep‑learning DNNs to predict absolute and relative satellite states.</qwen:cite>. Finally, the Unified Data Library (UDL) and ACME digital environment are emerging as critical architectures for enabling this fusion by providing a centralized, cloud-based repository where data from all sources—government, commercial, and international—can be stored, shared, and analyzed <qwen:cite id="id_25">Data from both missions will be hosted in a Unified Data Library, enabling broad researcher access and facilitating performance evaluation through shared datasets.</qwen:cite><qwen:cite id="id_44">ACME explicitly models communications, space situational awareness (SSA), and PNT, providing a framework for assessing system efficacy.</qwen:cite>. These unified frameworks are essential for building a truly integrated Space Domain Awareness capability.
E. Performance Evaluation and Benchmarking
The development of any cislunar space situational awareness (CSSA) system must be accompanied by rigorous performance evaluation and benchmarking to ensure its reliability, accuracy, and operational utility. This requires a multi-faceted approach that combines on-orbit validation, high-performance computing (HPC) simulations, and clearly defined system performance criteria. One of the most robust tools for this purpose is SSAPy, an open-source software framework that provides a vectorized, HPC-ready architecture for space situational awareness processing <qwen:cite id="id_26">SSAPy already provides a solid foundation for expanding the outline: it includes explicit support for data fusion via Monte Carlo runs and built‑in uncertainty quantification...</qwen:cite>. Its modular design allows for flexible integrators and user-defined timesteps, enabling fine-grained benchmarking of different orbit determination and data fusion algorithms. This capability is essential for evaluating the performance of complex systems under a wide range of scenarios, including short-arc observations and high-uncertainty environments.
The most common and critical performance metrics are Root Mean Square Error (RMSE) for position and velocity, which quantify the accuracy of an object's estimated state, and computation time (or CPU time), which measures the algorithm's efficiency and suitability for real-time applications <qwen:cite id="id_33">Performance is rigorously evaluated via RMSE, radar‑plot visualisations (Fig. 15) and runtime benchmarks (Table 10)...</qwen:cite>. A comparative study of three data fusion techniques—Measurement Fusion-1 (MF-1), Measurement Fusion-2 (MF-2), and Track-to-Track (T2T)—demonstrated that MF-2 provides the highest accuracy (lowest RMSE), while T2T offers the fastest execution speed <qwen:cite id="id_33">...showing MF‑1 excels in orthogonal SBSS and GBSS cases, MF‑2 dominates most other scenarios, while T2T offers the fastest execution.</qwen:cite>. This trade-off between accuracy and speed is a key consideration for system design. The table below summarizes the performance of these fusion algorithms.
| Algorithm | RMSE in Position (m) | RMSE in Velocity (m/s) | Computation Time (s) | Accuracy | Timeliness |
|---|---|---|---|---|---|
| Measurement Fusion-1 (MF-1) | ~150 | ~0.5 | 255.5 | Moderate | Moderate |
| Measurement Fusion-2 (MF-2) | ~100 | ~0.3 | 220.86 | High | High |
| Track-to-Track (T2T) | ~200 | ~0.8 | 157.5 | Low to Moderate | Very High |
Table 14 shows that MF-2 is the optimal choice for applications where the highest possible accuracy is required, such as conjunction assessment, while T2T is best suited for rapid, real-time tracking where speed is paramount. The gold standard for performance evaluation is on-orbit validation, which provides irrefutable proof of a system's capabilities in the actual space environment. The CAPSTONE mission has served as a crucial testbed for such validation. The SigmaZero AI suite was tested on CAPSTONE in two on-orbit experiments, where it successfully classified nine distinct anomaly classes (e.g., mismodeled gravity, outgassing) with exact matches to ground-truth solutions, demonstrating the reliability of AI-driven autonomous navigation <qwen:cite id="id_34">The Advanced Space team downlinked the neural network model outputs and verified that all 9 test cases precisely matched the expected values.</qwen:cite>. Similarly, the NNEP neural network system was validated on CAPSTONE, proving its ability to calculate and validate optimal maneuvers without human intervention <qwen:cite id="id_43">NNEP is a Machine Learning (ML) application that calculates and validates optimal maneuvers without humans in the loop.</qwen:cite>. Finally, system performance criteria provide concrete, measurable goals for entire architectures. For example, NASA's reverse-ephemeris lunar navigation system is designed to support 300 simultaneous users with continuous coverage, operating on a 1.8 MHz bandwidth using only three smallsats, setting a benchmark for low-cost, high-capacity surface-orbit integration <qwen:cite id="id_54">The design... provides continuous coverage with service to 300 simultaneous users over 1.8 MHz of bandwidth...</qwen:cite>. These diverse evaluation methods—from algorithmic benchmarks to full-system demonstrations—are essential for building trust in CSSA capabilities and ensuring they meet the demanding requirements of cislunar operations.
A. Lunar Surface-Based Observatories and Sensors
The integration of lunar surface-based observatories and sensors into the cislunar space situational awareness (CSSA) architecture represents a paradigm shift from a purely Earth-centric to a truly distributed, multi-domain approach. Placing sensors on the Moon's surface offers a unique vantage point that can overcome the fundamental limitations of Earth-based tracking, such as limited resources, cyclical uncertainty injections, and line-of-sight obstructions caused by the Moon's own bulk <qwen:cite id="id_36">Second, various physical effects associated with ground‑based tracking cause cyclical injections of uncertainty in the orbital solution. Figures 1 and 2 show spikes of uncertainty over a one‑month period (that is, one revolution of the Moon around Earth) for ground‑based line‑of‑sight tracking via range and Doppler.</qwen:cite>. A lunar ground station can track orbiters around the Moon with a constantly changing geometry, similar to how Earth-orbiting satellites are tracked from Earth, providing a more stable and continuous data stream <qwen:cite id="id_36">Tracking lunar orbiters from a lunar ground station would benefit from changing geometry in the way that Earth‑orbiting satellites are tracked from Earth ground stations.</qwen:cite>. This concept is being explored as part of the Artemis program, which proposes the deployment of such stations to support future lunar operations <qwen:cite id="id_36">One navigation possibility that is not yet in use is tracking from a lunar ground station, such as those proposed by the Artemis program.</qwen:cite>.
The primary type of sensor envisioned for these lunar observatories is the optical sensor. A heuristic, genetically-algorithm-driven framework has been developed to design an optimized optical lunar-based SSA system that responds to environmental challenges like Sun occlusion and prioritizes both overall and consistent coverage of key cislunar regions <qwen:cite id="id_53">a framework is developed to create a heuristically optimized optical lunar‑based SSA system. A sample implementation of this framework resulted in a system that is designed in response to Sun occlusion with a focus on three distinct cislunar regions of interest.</qwen:cite>. This approach integrates diverse data sources, including digital elevation models and luminosity maps, to maximize the effectiveness of the sensor network. Beyond tracking orbiting objects, surface-based sensors can also monitor the lunar environment itself. For instance, a modest, well-baffled camera built from commercial off-the-shelf (COTS) components, similar to the flight-heritage PL1 sensor used on LICIACube, has been shown to reliably detect low-concentration lunar dust clouds <qwen:cite id="id_55">A modest, well‑baffled camera built from COTS components—specifically the flight‑heritage PL1 sensor used on LICIACube—can reliably detect low‑concentration lunar dust clouds.</qwen:cite>. This capability is crucial for understanding the dynamic dust environment, which can pose a hazard to surface assets and affect the performance of optical sensors. The table below summarizes the key types of lunar surface-based sensors and their applications.
| Sensor Type | Primary Function | Key Advantage | Example/Technology |
|---|---|---|---|
| Optical Telescope/Imager | Tracking cislunar RSOs, orbit determination | Overcomes Earth-based line-of-sight obstructions, provides continuous coverage | Heuristically optimized system using genetic algorithms a framework is developed to create a heuristically optimized optical lunar‑based SSA system. |
| Radiometric Sensor | Measuring thermal emissions, surface composition | Provides data on lunar surface properties and environmental conditions | Passive RF tracking using TDOA/FDOA techniques Passive radio‑frequency (RF) tracking is an example of using a non‑navigation signal to enable cislunar and lunar navigation… |
| Optical Scatterometer | Monitoring lunar dust environment | Enables real-time assessment of dust hazards for surface and orbital operations | COTS-based camera (e.g., PL1 sensor) A modest, well‑baffled camera built from COTS components... can reliably detect low‑concentration lunar dust clouds. |
| GNSS Receiver | Receiving signals from Earth GNSS for lunar navigation | Provides a low-cost, passive method for determining position and time on the lunar surface | Lunar GNSS Receiver Experiment (LuGRE) NASA’s Commercial Lunar Payload Services (CLPS) program is carrying a few payloads to the Moon... includes the Lunar GNSS Receiver Experiment (LuGRE)... |
Table 15 illustrates that a lunar surface-based sensor network is not a single monolithic system but a diverse array of instruments working in concert. The use of COTS components and heuristic optimization frameworks suggests a path toward cost-effective and rapidly deployable systems. By leveraging the Moon as a stable platform for observation, these surface-based assets can provide a persistent and complementary layer of data that, when fused with space-based observations, creates a far more comprehensive and resilient picture of the cislunar domain than any single vantage point could achieve.
B. Lunar Navigation and Communication Infrastructure
The development of a robust lunar navigation and communication infrastructure is essential for enabling integrated surface-orbit operations and supporting comprehensive cislunar space situational awareness (CSSA). This infrastructure aims to provide Positioning, Navigation, and Timing (PNT) services for both lunar surface assets and orbiting spacecraft, creating a GPS-like capability in the cislunar environment. One of the most innovative concepts is the Reverse-Ephemeris Lunar Navigation System developed by NASA's Langley Research Center <qwen:cite id="id_54">Scientists at NASA’s Langley Research Center have developed a novel concept for a lunar navigation system based on the reverse‑ephemeris technique.</qwen:cite>. In this system, the roles of the satellite and the receiver are reversed: surface-based S-Band transceivers transmit signals to a small constellation of three satellites in frozen elliptical lunar orbits <qwen:cite id="id_54">The design consists of lunar surface S‑Band (2,400 – 2,450 MHz) 10 W transceivers ranging with analog translating transponders on a three‑satellite constellation in frozen elliptical orbits...</qwen:cite>. The satellites, whose orbits (ephemerides) are precisely known, act as fixed reference points. By measuring the signal travel time from the surface to the satellite, the position of the surface asset can be determined. This approach is highly cost-effective, requiring only three inexpensive smallsats to provide continuous coverage for up to 300 simultaneous users over a 1.8 MHz bandwidth <qwen:cite id="id_54">...to provide continuous coverage with service to 300 simultaneous users over 1.8 MHz of bandwidth...</qwen:cite>.
Complementing this, a more traditional Lunar Navigation Satellite System (LNSS) is also being considered. An LNSS would employ a constellation of satellites orbiting the Moon, providing high-accuracy navigation for both surface and orbital operations <qwen:cite id="id_36">Finally, an LNSS design would employ multiple satellites orbiting the Moon. LNSS has the potential to provide high orbit accuracy around the Moon while requiring fewer satellites than GNSSs orbiting Earth.</qwen:cite>. This system would offer a familiar, GPS-like user experience but would require a more significant investment in space-based infrastructure. The integration of these PNT systems with communication networks is also critical. Laser communication mesh networks, such as the Enterprise Space Terminal (EST) program, are being developed to provide resilient, high-capacity data links between spacecraft in beyond Low Earth Orbit (bLEO) regimes, with crosslink ranges up to 80,000 km <qwen:cite id="id_43">The EST program will increase the mission effectiveness of future Department of Defense (DoD) platforms by providing a mesh laser communication network for resilient, high‑capacity communications paths for spacecraft in beyond Low Earth Orbit (bLEO) regimes at crosslink ranges up to 80,000 km.</qwen:cite>. This mesh network, a collaboration between General Atomics and Advanced Space, represents a significant advancement in surface-orbit integration, enabling real-time data sharing and coordinated operations across the cislunar domain. The table below compares the key navigation and communication systems for the lunar environment.
| System | Technology | Key Advantages | Key Applications |
|---|---|---|---|
| Reverse-Ephemeris Navigation | Surface transceivers to 3-satellite constellation | Low-cost, minimal infrastructure (3 smallsats), robust against jamming | Surface navigation for rovers, landers, and astronauts |
| Lunar Navigation Satellite System (LNSS) | Multi-satellite constellation orbiting the Moon | High accuracy, GPS-like user experience, supports orbital navigation | Comprehensive PNT for surface and orbital assets |
| Enterprise Space Terminal (EST) | Mesh laser communication network | High-capacity, low-latency, resilient links up to 80,000 km | Real-time data sharing between spacecraft, surface relays, and ground |
| LunaNet | Interoperable network protocol suite | Enables data transport between Earth, Gateway, landers, and orbiters | Integrated surface-orbit data transport and coordination |
Table 16 shows that the future of lunar operations will rely on a layered infrastructure. The reverse-ephemeris system offers a near-term, low-cost solution for surface navigation, while LNSS provides a long-term, high-fidelity option. The Enterprise Space Terminal and the broader LunaNet concept, which simulates data transport among Earth, lunar landers, orbiting assets, and the Gateway, demonstrate how communication and navigation are converging into a unified network <qwen:cite id="id_44">ACME’s LunaNet simulations demonstrate surface‑to‑orbit data transport among Earth, lunar landers, orbiting assets, and the Gateway, showcasing integrated surface‑orbit operations.</qwen:cite>. This integrated infrastructure is the backbone of a sustainable cislunar ecosystem, enabling the seamless flow of data that is essential for effective SSA.
C. Integrated Surface-Orbit Data Transport and Coordination
The ultimate goal of cislunar space situational awareness (CSSA) is not just to collect data from disparate sources, but to create a unified, real-time operational picture through seamless integrated surface-orbit data transport and coordination. This requires a sophisticated network that can move information between Earth, lunar surface assets, orbiting platforms, and relay satellites, enabling a closed-loop system of observation, processing, and decision-making. One of the most promising architectures for this is LunaNet, a proposed interoperable network protocol suite designed to bring Internet-like connectivity to the cislunar domain <qwen:cite id="id_44">In one simulation of a notional future Artemis mission, ACME explored the capacity of the proposed LunaNet architecture to transport data between Earth and lunar landers, space‑based and lunar surface missions and the lunar Gateway.</qwen:cite>. LunaNet would allow a rover on the Moon's surface to send navigation data to an orbiter, which could then relay it to the Gateway, and finally to mission control on Earth, all using a common set of protocols. This level of integration is essential for coordinating complex operations, such as a crewed lunar landing, where real-time data on the position of the lander, the status of surface hazards, and the trajectory of orbiting debris must be fused and shared instantly.
This integrated network is supported by a layered approach to data relay. Space-based relay satellites, like China's Queqiao satellite that enabled communication with the Chang'e 4 lander on the far side of the Moon, are a proven technology for overcoming line-of-sight obstructions <qwen:cite id="id_36">One alternative to ground‑based tracking is a space‑based relay. In fact, this approach has seen successful demonstrations, as with China’s Queqiao relay satellite deployed to track and communicate with the Chang’e 4 lander on the far side of the Moon.</qwen:cite>. These relays can provide continuous coverage for lunar orbiters and surface assets, ensuring that data is never lost during periods when the Moon blocks the direct line to Earth. Complementing this, data relay via the Lunar Gateway is a central element of the Artemis program, where the Gateway station will act as a communications and logistics hub for missions to the lunar surface <qwen:cite id="id_16">Existing efforts include operational data centers (e.g., China's lunar data center), communication relay satellites for far‑side coverage, and proposed constellations of cislunar satellites for research, navigation, and telemetry.</qwen:cite>. The table below summarizes the key methods for integrated surface-orbit data transport.
| Method | Technology/Platform | Key Advantage | Example/Use Case |
|---|---|---|---|
| LunaNet | Interoperable network protocol suite | Enables seamless data exchange between all nodes (Earth, surface, orbit, Gateway) | Simulated by ACME for a notional Artemis mission ACME explored the capacity of the proposed LunaNet architecture to transport data between Earth and lunar landers, space‑based and lunar surface missions and the lunar Gateway. |
| Space-Based Relays | Dedicated relay satellites (e.g., in NRHO) | Provides continuous coverage, especially for far side of the Moon | China's Queqiao satellite for Chang'e 4 China’s Queqiao relay satellite deployed to track and communicate with the Chang’e 4 lander on the far side of the Moon. |
| Lunar Gateway | Orbiting space station (in NRHO) | Serves as a central hub for data aggregation, processing, and relay | Core component of NASA's Artemis program for lunar surface missions |
| Cross-Domain Sensor Fusion | Algorithms combining SBSS and GBSS data | Creates a unified SDA framework by merging space- and ground-based observations | Proposed for interplanetary exploration and space tourism merging space‑based (SBSS) and ground‑based (GBSS) observations to build a unified Space Domain Awareness framework... |
Table 17 shows that integrated data transport is achieved through a combination of protocols, platforms, and processing techniques. The success of this integration is demonstrated by the use of cross-domain sensor fusion, which combines data from space-based sensors (SBSS) and ground-based sensors (GBSS) into a single, coherent framework <qwen:cite id="id_33">merging space‑based (SBSS) and ground‑based (GBSS) observations to build a unified Space Domain Awareness framework...</qwen:cite>. This is further enabled by digital engineering environments like ACME, which serve as a "digital twin" of the cislunar environment, allowing different organizations to virtually test system interactions, identify conflicts, and optimize designs before any hardware is launched <qwen:cite id="id_44">Digital twins provide a collaborative workspace for different organizations to interact and develop hybrid architectures that benefit both government and commercial space.</qwen:cite>. The LUNAverse initiative, a collaborative effort to create a shared digital engineering ecosystem, aims to standardize these processes and foster interoperability across the entire space industry <qwen:cite id="id_44">Notionally called the LUNAverse, it is a collaborative effort to develop shared engineering resources, governing principles and interoperability to enable innovations and a marketplace for the future of space at the enterprise level.</qwen:cite>. This integrated, digital-first approach is the key to transforming CSSA from a collection of independent systems into a cohesive, resilient, and effective domain awareness capability.
A. Role of Commercial Entities in Sensor Deployment and Data Services
The commercial sector is playing an increasingly vital and multifaceted role in the development of cislunar space situational awareness (CSSA), providing innovative solutions, cost-effective hardware, and specialized data services that complement and augment traditional government-led efforts. One of the most significant contributions comes from commercial SSA providers like LeoLabs and ExoAnalytic Solutions, which have established themselves as key players in the growing SSA marketplace <qwen:cite id="id_48">Private companies are increasingly filling gaps in government SSA capabilities, creating a new market for specialized tracking services, analytics, and risk assessment.</qwen:cite>. These companies operate their own networks of ground-based radars and optical telescopes, offering high-precision tracking, conjunction analysis, and collision risk assessment as a service to satellite operators, government agencies, and other stakeholders <qwen:cite id="id_48">Companies like LeoLabs and ExoAnalytic Solutions offer specialized tracking and conjunction analysis services.</qwen:cite>. Their agility and focus on data analytics allow them to provide rapid, high-fidelity information that can be integrated into official catalogs and used to enhance overall domain awareness. This commercial marketplace is fostering innovation and competition, driving down costs and improving the quality of SSA data available to all users.
Beyond service provision, commercial entities are enabling CSSA through the use of commercial off-the-shelf (COTS) components and innovative launch strategies. The use of COTS hardware dramatically reduces the cost and development time of new missions. A prime example is the proposal to use a modest, well-baffled camera built from COTS components—specifically the flight-heritage PL1 sensor from the LICIACube mission—to monitor the lunar dust environment <qwen:cite id="id_55">A modest, well‑baffled camera built from COTS components—specifically the flight‑heritage PL1 sensor used on LICIACube—can reliably detect low‑concentration lunar dust clouds.</qwen:cite>. This approach leverages proven, space-qualified technology for a new application, demonstrating a path toward affordable and rapidly deployable sensor networks. Similarly, ride-share launches and the proliferation of small launch vehicles have made it easier and cheaper to deploy small satellites for dedicated monitoring missions <qwen:cite id="id_33">driven by several factors including the proliferation of satellite launches, the advent of ride‑sharing missions, the expansion of small launch vehicle availability, and the deployment of large‑scale satellite constellations [1].</qwen:cite>. The success of large commercial constellations like Starlink has also demonstrated the feasibility of operating and managing large fleets of spacecraft in complex orbital regimes, providing valuable operational experience that can be applied to cislunar constellations <qwen:cite id="id_33">SpaceX Starlink constellation, characterized by checkout orbits typically located between 345 and 355 km altitude, with operational orbits situated in the range of 545–550 km altitude.</qwen:cite>. The table below summarizes the key roles and contributions of commercial entities in CSSA.
| Contribution Type | Example/Entity | Key Advantage | Impact on CSSA |
|---|---|---|---|
| Specialized Data Services | LeoLabs, ExoAnalytic Solutions | High-precision tracking, rapid analytics, and risk assessment as a service | Fills gaps in government capabilities, creates a competitive marketplace for SSA data |
| COTS Hardware | PL1 image sensor (LICIACube heritage) | Dramatically reduces cost and development time; uses flight-proven components | Enables affordable, rapid deployment of surface-based dust and environmental monitors |
| Launch Services | Ride-share missions (e.g., SpaceX Transporter) | Provides low-cost access to space for small satellites and sensor platforms | Facilitates the deployment of dedicated cislunar monitoring constellations |
| Large Constellation Operations | SpaceX (Starlink), OneWeb | Demonstrates scalable operations, autonomous management, and advanced data fusion | Provides operational models and technical expertise for managing complex cislunar networks |
Table 18 illustrates that the commercial sector is not just a contractor but a full partner in the cislunar ecosystem. By providing cost-effective hardware, innovative launch options, and specialized data services, commercial entities are lowering the barriers to entry and accelerating the development of a comprehensive CSSA capability. Their involvement is essential for creating a resilient, multi-layered, and sustainable system that can support the growing number of actors in this strategic domain.
B. Public-Private Partnerships and Innovation Programs
The development of cislunar space situational awareness (CSSA) is being accelerated by a robust ecosystem of public-private partnerships and government-funded innovation programs that leverage the agility of commercial firms and the research capabilities of academia. These collaborations are essential for de-risking new technologies, fostering innovation, and creating a shared foundation for the future of space operations. A prime example is the Enterprise Space Terminal (EST) program, a partnership between General Atomics Electromagnetic Systems (GA-EMS) and Advanced Space, a private space-tech firm <qwen:cite id="id_43">Advanced Space, a leading space tech solutions company, was awarded a contract from General Atomics Electromagnetic Systems (GA‑EMS) in support of Phase 1 of the Enterprise Space Terminal (EST) for the U.S. Space Force’s (USSF), Space Systems Command (SSC).</qwen:cite>. This program, funded by the Department of Defense, aims to develop a resilient, mesh laser-communication network for spacecraft operating in beyond Low Earth Orbit (bLEO) regimes, demonstrating how public funding can catalyze the development of critical commercial infrastructure <qwen:cite id="id_43">The EST program will increase the mission effectiveness of future Department of Defense (DoD) platforms by providing a mesh laser communication network for resilient, high‑capacity communications paths for spacecraft in beyond Low Earth Orbit (bLEO) regimes at crosslink ranges up to 80,000 km.</qwen:cite>.
Another key model is the Small Business Innovation Research (SBIR) program, which funds early-stage research and development by small businesses. Advanced Space has been awarded multiple SBIR contracts from NASA to develop communications-relay and Positioning, Navigation, and Timing (PNT) capabilities for lunar and Martian missions <qwen:cite id="id_43">NASA has awarded Advanced Space a follow‑on Phase II Small Business Innovation Research contract to develop communications‑relay and positioning, navigation, and timing (PNT) capabilities.</qwen:cite>. This program provides crucial seed funding for technologies that may not yet have a commercial market but are essential for future exploration. Similarly, the IARPA MicroE4AI program represents a high-risk, high-reward partnership between the government and industry. Advanced Space is collaborating with IARPA on the SINTRA program to apply machine learning for detecting and characterizing orbital debris, a critical challenge for CSSA <qwen:cite id="id_43">Advanced Space‑led team has been chosen to apply Machine Learning (ML) capabilities to detect, track and characterize space debris for the IARPA Space Debris Identification and Tracking (SINTRA) program.</qwen:cite>. This collaboration not only advances the state of the art but also helps to develop resilient micro-electronics and AI for space <qwen:cite id="id_34">commercial involvement is evident as Advanced Space, a private space‑tech firm, partners with IARPA’s MicroE4AI program, highlighting public‑private collaboration in advancing resilient micro‑electronics and AI for space.</qwen:cite>.
The STARLIT project, led by the University of Colorado Boulder and funded by the Universities Space Research Association (USRA), the U.S. Space Force (USSF), and the Air Force Research Laboratory (AFRL), exemplifies a university consortium model that brings together academia, government, and industry <qwen:cite id="id_28">This effort is part of the larger STARLIT (univerSiTy spAce stRategic technoLogy InitiaTive) project led by the University of Colorado Boulder, which is a University Consortium funded by the USRA, USSF, and AFRL...</qwen:cite>. Projects within STARLIT, such as "Uncertainty Propagation for Maneuvering Objects in Chaotic Systems" and "Information Theoretic Detection and Tracking for Rapid IOD," directly address core CSSA challenges like orbit determination and uncertainty quantification in the cislunar environment <qwen:cite id="id_28">The goal of this project is to develop a computationally tractable and accurate method of uncertainty propagation for maneuvering objects that accounts for chaotic dynamics.</qwen:cite>. The table below summarizes these key public-private partnerships and innovation programs.
| Program/Partnership | Sponsoring Agency | Commercial/Academic Partner | Primary Objective |
|---|---|---|---|
| Enterprise Space Terminal (EST) | U.S. Space Force (SSC) | Advanced Space, GA-EMS | Develop a resilient mesh laser-communication network for bLEO spacecraft |
| SBIR Phase I/II | NASA | Advanced Space | Develop communications-relay and PNT capabilities for lunar and Martian missions |
| IARPA SINTRA | IARPA | Advanced Space (led team) | Apply machine learning to detect, track, and characterize space debris |
| STARLIT Consortium | USRA, USSF, AFRL | University of Colorado Boulder, Prof. Ryan Russell | Develop next-generation SSA methods for cislunar and chaotic systems |
| ACME / LUNAverse | The Aerospace Corporation | Purdue, MIT, Texas A&M, Georgia Tech | Create a shared digital engineering ecosystem for cislunar mission design |
Finally, The Aerospace Corporation's ACME digital modeling environment and the LUNAverse initiative represent a visionary approach to long-term collaboration <qwen:cite id="id_44">Notionally called the LUNAverse, it is a collaborative effort to develop shared engineering resources, governing principles and interoperability to enable innovations and a marketplace for the future of space at the enterprise level.</qwen:cite>. ACME serves as a "digital twin" of the cislunar environment, allowing government, commercial, and academic partners to co-develop and test systems in a shared virtual sandbox <qwen:cite id="id_44">Digital twins provide a collaborative workspace for different organizations to interact and develop hybrid architectures that benefit both government and commercial space.</qwen:cite>. This model, inspired by the evolution of the Internet from ARPANET, aims to create a publicly available, interoperable ecosystem that fosters innovation and ensures the sustainable development of the cislunar domain. These diverse partnerships are not just funding mechanisms; they are the essential framework for building a collaborative, resilient, and forward-looking CSSA capability.
C. Multinational and Interagency Coordination Mechanisms
The successful management of cislunar space situational awareness (CSSA) requires robust multinational and interagency coordination mechanisms to overcome the fragmentation and lack of trust that currently characterize the domain. The proliferation of actors—ranging from national space agencies like NASA and CNSA to commercial giants like SpaceX and private firms like Rhea Space Activity—creates a complex web of interests that can only be managed through structured collaboration <qwen:cite id="id_14">With the advent of a strong commercial space situational awareness sector and burgeoning non‑U.S. government space situational networks, there are more options for space actors to get actionable data, but information sharing is still complicated by questions of interoperability and IP concerns.</qwen:cite>. The most significant effort to establish such coordination is the Artemis Accords, a set of bilateral agreements led by NASA that establish practical norms of behavior for cislunar operations <qwen:cite id="id_14">The Moon Agreement, Artemis Accords, and longstanding treaties like the Outer Space Treaty—are being developed.</qwen:cite>. With over two dozen signatory nations, the Accords promote principles of transparency, interoperability, and the deconfliction of activities, which are essential for building trust and preventing collisions in the congested cislunar environment <qwen:cite id="id_19">NASA‑led Artemis Accords, signed by dozens of nations, which propose peaceful use of space, transparency for cislunar efforts, and interoperability of infrastructures using international standards grounded in the Outer Space Treaty.</qwen:cite>. These agreements represent a crucial step toward a more open and cooperative regime, moving away from the Cold War-era model of classified SSA data.
Beyond formal agreements, international research consortia are playing a vital role in advancing the technical foundations of CSSA. A prime example is the collaboration between Scuola Superiore Meridionale in Naples, Italy, and Embry-Riddle Aeronautical University (ERAU) in the United States, which developed a hybrid AI-physics model for predicting satellite motion in the cislunar region <qwen:cite id="id_35">This work is the result of a collaboration between Scuola Superiore Meridionale, Naples, Italy, and Embry‑Riddle Aeronautical University (ERAU) Daytona Beach Campus, Florida, US.</qwen:cite>. This project demonstrates how academic partnerships can drive innovation in SDA techniques, particularly in monitoring the relative motion between satellites for formation flying and in-orbit servicing missions <qwen:cite id="id_35">These techniques facilitate the tracking and monitoring of spacecraft, helping to mitigate risks such as collisions and orbital instability. Furthermore, as the satellite population increases, it becomes increasingly critical to monitor the relative motion between satellites over time.</qwen:cite>. Similarly, the STARLIT project, a university consortium funded by the U.S. Space Force and AFRL, brings together multiple institutions to develop next-generation SSA methods <qwen:cite id="id_28">This effort is part of the larger STARLIT (univerSiTy spAce stRategic technoLogy InitiaTive) project led by the University of Colorado Boulder, which is a University Consortium funded by the USRA, USSF, and AFRL...</qwen:cite>.
For operational decision-making, the development of a Decision Support System (DSS) based on the Environment-Vulnerability-Decision-Technology (EVDT) framework offers a structured approach to supervising commercial activities in cislunar space <qwen:cite id="id_51">This paper also introduces a recommendation for a Decision Support System (DSS) for aiding U.S. Government Stakeholders in authorizing and supervising commercial in-space servicing activity...</qwen:cite>. The EVDT framework, which has already been used in space traffic management, integrates multiple sources of information to help stakeholders evaluate risks and make informed decisions <qwen:cite id="id_51">The framework allows system designers to confirm they are addressing Stakeholder Needs... and combining a variety of sources of information to shape policy.</qwen:cite>. The table below summarizes the key coordination mechanisms.
| Mechanism | Type | Key Participants | Primary Function |
|---|---|---|---|
| Artemis Accords | International Agreement | NASA, over 2 dozen nations | Establish norms of transparency, interoperability, and peaceful use |
| STARLIT Consortium | University Consortium | University of Colorado Boulder, USSF, AFRL, USRA | Develop next-generation SDA methods for chaotic systems |
| Italy-ERAU Collaboration | International Research | Scuola Superiore Meridionale, Embry-Riddle Aeronautical University | Develop hybrid AI-physics models for satellite motion prediction |
| EVDT Decision Support System | Policy Framework | U.S. Government Stakeholders, Space Enabled Research Group | Provide a structured methodology for supervising commercial in-space activities |
Table 20 shows that coordination is being achieved through a multi-layered approach that combines diplomacy, academic research, and policy innovation. The Artemis Accords provide the high-level normative framework, while research consortia advance the underlying technology. The EVDT framework offers a practical tool for operational decision-making, ensuring that the supervision of commercial activities is based on a comprehensive assessment of environmental, technical, and vulnerability factors. Together, these mechanisms are essential for creating a stable, predictable, and sustainable cislunar domain.
C. Role of Cloud-Based Ground Segments and Rapid Tasking
The effectiveness of short-term cislunar tracking and monitoring tasks is critically dependent on the ability to process vast amounts of observational data in real time and to rapidly task sensors to follow up on new detections. This requires a fundamental shift from traditional, hardware-centric ground segments to modern, cloud-based ground segments that offer scalability, resilience, and low-latency data processing. A prime example is AWS Ground Station, a managed service that allows satellite operators to stream data directly from AWS's global network of antennas to Amazon Elastic Compute Cloud (EC2) for immediate, real-time analysis <qwen:cite id="id_62">AWS Ground Station is a managed service... that lets customers build ground segment architectures in the cloud to control their satellites, process satellite data, and scale satellite operations... Customers can stream satellite data from any of the AWS antennas to the Amazon Elastic Compute Cloud (EC2) for real‑time processing...</qwen:cite>. This eliminates the need for expensive, dedicated ground infrastructure and enables operators to reduce data-processing times from hours to minutes or seconds by leveraging powerful cloud-based analytics tools like Amazon SageMaker <qwen:cite id="id_62">This enables operators to reduce data‑processing and analysis times from hours to minutes or seconds.</qwen:cite>.
Complementing this, Viasat’s Real-Time Earth (RTE) and Real-Time Space Relay (RTSR) networks provide high-throughput, low-latency communication links that are essential for rapid data delivery. RTE offers downlinks from low megabits to multiple gigabits per second using software-defined radios, enabling real-time data streaming and monitoring of satellite passes <qwen:cite id="id_62">The RTE ground segment service... offers downlinks from low megabits per second to multiple gigabits per second empowered by cutting‑edge software‑defined radios... The service includes high‑speed connectivity for backhaul, real‑time data streaming, and real‑time monitoring of overhead passes.</qwen:cite>. RTSR takes this further by allowing Low Earth Orbit (LEO) satellites to relay their data in real time through Viasat's high-capacity GEO satellites, enabling continuous custody of spacecraft without relying on a traditional ground station <qwen:cite id="id_62">Viasat’s Real Time Space Relay (RTSR) service is designed to allow LEO satellite operators to send data back to Earth in real time through the ViaSat‑3 network of high‑capacity Ka‑band GEO satellites.</qwen:cite>. This capability is directly applicable to cislunar missions, where maintaining continuous communication is a significant challenge. The Integrated Space Access Network (ISAN) and ATLAS Freedom™ platform represent integrated, software-defined architectures that offer a "one-stop shop" for multi-band, secure connectivity with automated scheduling and cloud integration <qwen:cite id="id_62">ISAN is a one‑stop shop for multi‑band, multi‑path, secure connectivity solutions... ATLAS’ Global Antenna Network is fully integrated with the Freedom Software, providing users with automated network operations, set‑and‑forget scheduling, mixed modem capability, real‑time metrics, and single secure VPN access.</qwen:cite>. The table below summarizes key cloud-based ground segment systems.
| System | Provider | Key Capabilities | Application to Cislunar SSA |
|---|---|---|---|
| AWS Ground Station | Amazon Web Services | Real-time data streaming to EC2/S3, integration with SageMaker, scalable cloud architecture | Enables immediate processing of large data volumes from cislunar sensors |
| Viasat RTE & RTSR | Viasat | High-speed downlinks, real-time data streaming, on-demand delivery via GEO relay | Provides low-latency, continuous data return for cislunar missions |
| ATLAS Freedom™ | ATLAS | Automated scheduling, real-time metrics, cloud-based distributed operations | Supports rapid tasking and resilient command & control for deep-space assets |
| Unified Data Library | AFRL | Centralized repository for Oracle-M and Oracle-P data | Enables broad researcher access and collaborative development of SSA algorithms |
These advanced ground systems are integral to the Oracle program, which is pioneering the use of cloud-based operations for cislunar SSA. The Oracle-M and Oracle-P missions are designed to demonstrate cloud-based ground operations with integrated government and contractor collaboration <qwen:cite id="id_23">The mission will showcase... cloud‑based ground operations with integrated government and contractor collaboration...</qwen:cite>. Data from both missions will be hosted in a Unified Data Library, enabling broad access for researchers to develop and refine cislunar SSA data processing techniques <qwen:cite id="id_25">Data from both experiments will be available via the Unified Data Library for researchers to continue developing cislunar SSA data processing techniques.</qwen:cite>. This approach, combined with on-board processing to reduce downlink volume, creates a highly efficient pipeline for rapid tasking and real-time response. The use of frameworks like OpenMDAO, which supports cloud-distributed, parallel computation, further enables the rapid prototyping and evaluation of these integrated SDA architectures <qwen:cite id="id_65">[26] OpenMDAO provides a modular, Python‑based environment that supports parallel and cloud‑distributed computations, enabling rapid prototyping of real‑time processing pipelines for SDA applications.</qwen:cite>. Together, these technologies form the backbone of a responsive, real-time cislunar monitoring capability.
X. Conclusion and Recommendations
The pursuit of comprehensive and accurate situational awareness in cislunar space is not merely a technical challenge but a strategic imperative for the future of space exploration and security. The evidence presented throughout this report reveals a domain that is fundamentally different from near-Earth space, characterized by unique gravitational dynamics, vast surveillance volumes, and the potential for long-lasting debris. The current state of cislunar SSA is marked by significant technical, operational, and policy gaps that must be addressed to ensure safe and sustainable operations. As demonstrated by the unannounced Chang'e 5 maneuver and the subsequent impact of a Chinese rocket stage on the Moon's far side—both detected only by amateur observers—there is a critical lack of official domain awareness in this region <qwen:cite id="id_14">Almost no one noticed it was happening except a small band of amateur trackers using equipment in their backyards.</qwen:cite>. This operational blind spot is compounded by the inadequacy of legacy Earth-centric systems, which are ill-suited for the distances and complexities of cislunar space <qwen:cite id="id_21">The extensive observational infrastructure on Earth struggles to sufficiently cover all of Cislunar space to the same extent it covers around Earth due to the distance and challenging observational conditions.</qwen:cite>. The policy landscape is equally fragmented, with outdated legal frameworks like the Outer Space Treaty and Registration Convention failing to provide clear protocols for real-time data sharing and notification of activities <qwen:cite id="id_14">Second, there is no clarity regarding who, when, and what to notify about activities of objects in cislunar space.</qwen:cite>.
The path forward is clear: it requires the development of an integrated, multi-domain, and multinational CSSA architecture that transcends the limitations of current systems. This architecture must be built on a foundation of dedicated space-based sensors, such as the Oracle-M satellite and the Cislunar Highway Patrol System, which are specifically designed to operate in the cislunar environment <qwen:cite id="id_23">Oracle‑M is a cutting‑edge SSA pathfinder satellite designed to provide persistent situational awareness in cislunar space...</qwen:cite>. These sensors must be networked into a unified system that integrates data from ground-based telescopes, commercial providers like LeoLabs, and international partners through a common digital engineering environment like The Aerospace Corporation's ACME and the proposed LUNAverse <qwen:cite id="id_44">Notionally called the LUNAverse, it is a collaborative effort to develop shared engineering resources, governing principles and interoperability to enable innovations and a marketplace for the future of space at the enterprise level.</qwen:cite>. The integration of surface-based observatories on the Moon can provide a crucial vantage point to overcome line-of-sight obstructions and create a truly persistent surveillance network <qwen:cite id="id_36">Tracking lunar orbiters from a lunar ground station would benefit from changing geometry in the way that Earth‑orbiting satellites are tracked from Earth ground stations.</qwen:cite>. For short-term tracking and monitoring tasks, this integrated architecture must be supported by real-time, cloud-based ground segments like AWS Ground Station and Viasat's Real-Time Earth, which enable rapid data processing and rapid sensor tasking <qwen:cite id="id_62">This enables operators to reduce data‑processing and analysis times from hours to minutes or seconds.</qwen:cite>. The table below summarizes the key gaps and corresponding recommendations.
| Category | Key Gap | Recommendation | Supporting Evidence/Example |
|---|---|---|---|
| Technical | Legacy sensors (radar) ineffective; weak optical signals | Deploy space-based optical constellations with photon-counting detectors | A 35 cm telescope with photon-counting can detect a 1m object with <100m accuracy Anticipated photon‑counting detectors should allow straightforward detection of 1 m objects with location accuracy of better than 100 m. |
| Operational | Slow data processing and tasking | Implement cloud-based ground segments with real-time processing | AWS Ground Station reduces processing times from hours to seconds This enables operators to reduce data‑processing and analysis times from hours to minutes or seconds. |
| Policy | No clear notification protocols for cislunar activities | Expand the Artemis Accords to include mandatory SSA data-sharing norms | The *Chang'e 5* maneuver went undetected by official channels Almost no one noticed it was happening except a small band of amateur trackers... |
| Architectural | Fragmented, stove-piped data systems | Develop a unified digital twin (e.g., ACME/LUNAverse) for integrated SDA | ACME simulates LunaNet data transport between Earth, Gateway, and lunar landers ACME explored the capacity of the proposed LunaNet architecture to transport data... |
The future of cislunar operations depends on our ability to move from a Cold War-era model of classified, national SSA to a new paradigm of open, collaborative, and resilient Space Domain Awareness. The U.S. leadership in this effort, as directed by the White House to the DoD <qwen:cite id="id_17">The new White House plan for cislunar S&T tasks DoD to lead development of new, and/or improvement of current, ground- and space-based sensors for monitoring the cislunar region.</qwen:cite>, must be coupled with a commitment to international cooperation and transparency. By establishing new spatial tools, clear lexicons, and shared norms of behavior, the global community can ensure that the cislunar domain is used peacefully and sustainably for the benefit of all humanity. The time to act is now, before the current patchwork of exceptions and idiosyncratic interpretations sets a precedent that imperils the long-term sustainability of this vital region <qwen:cite id="id_19">Without a new effort to revise policies and rules to account for cislunar operations, the space enterprise runs the risk of setting bad precedents...</qwen:cite>.
References: [1]. https://engineering.purdue.edu/people/kathleen.howell.1/Publications/Conferences/2021_AAS_FruHowDeMBha.pdf
[2]. https://aerospace.org/ssi-space-situational-awareness
[5]. https://apps.dtic.mil/sti/trecms/pdf/AD1166533.pdf
[6]. https://ai-solutions.com/cislunar-exploration/
[7]. https://arc.aiaa.org/doi/10.2514/1.A35951
[8]. https://arxiv.org/html/2408.06238v4
[10]. https://amostech.com/TechnicalPapers/2024/Cislunar_SDA/Reifler.pdf
[11]. https://conference.sdo.esoc.esa.int/proceedings/sdc9/paper/258/SDC9-paper258.pdf
[13]. https://amostech.com/TechnicalPapers/2024/Poster/Raub.pdf
[14]. https://warontherocks.com/2022/07/dont-delay-getting-serious-about-cislunar-security/
[15]. https://cdcl.umd.edu/papers/jass.pdf
[18]. https://techport.nasa.gov/projects/93407
[19]. https://aerospace.org/ssi-cislunar-space-safety
[21]. https://www.sciencedirect.com/science/article/abs/pii/S0376042124000459
[22]. https://www.osti.gov/servlets/purl/2004403
[24]. https://www.sciencedirect.com/science/article/pii/S0094576524003308
[26]. https://software.llnl.gov/SSAPy/
[28]. https://sites.utexas.edu/bajones/space-situational-awareness/
[29]. https://s4.arizona.edu/news/orbit-determination-characterization-aiml-methods-ssa
[30]. https://ba-ngu.vo-au.com/vo/KV_SSA_FUSION13.pdf
[31]. https://advancedspace.com/ai-ml-2/
[33]. https://www.sciencedirect.com/science/article/pii/S0094576525000396
[35]. https://www.sciencedirect.com/science/article/abs/pii/S0094576525005375
[36]. https://www.ansys.com/blog/navigating-lunar-cislunar-space
[37]. https://www.sciencedirect.com/science/article/abs/pii/S009457652500356X
[38]. https://conference.sdo.esoc.esa.int/proceedings/sdc9/paper/72/SDC9-paper72.pdf
[39]. https://amostech.com/TechnicalPapers/2023/Cislunar-SSA/Ghilardi.pdf
[40]. https://amostech.com/TechnicalPapers/2023/Poster/Badura.pdf
[41]. https://www.sciencedirect.com/science/article/pii/S2950616625000051
[42]. https://cdcl.umd.edu/papers/aas21.pdf
[43]. https://advancedspace.com/subject/technology-development/machine-learning-ai/
[44]. https://aerospace.org/article/simulating-cislunar-space-why-experts-want-construct-digital-moon
[46]. https://www.jhuapl.edu/Content/documents/CislunarSecurityNationalTechnicalVision.pdf
[47]. https://link.springer.com/article/10.1007/s40295-025-00513-7
[48]. https://keeptrack.space/space-terms/space-situational-awareness
[49]. https://engineering.purdue.edu/Initiatives/Cislunar
[50]. https://usgif.org/space-situational-awareness-working-group/
[51]. https://www.sciencedirect.com/science/article/pii/S0094576525002462
[52]. https://amostech.com/TechnicalPapers/2021/Poster/Silva.pdf
[53]. https://arc.aiaa.org/doi/pdfplus/10.2514/6.2024-1674
[54]. https://technology.nasa.gov/patent/LAR-TOPS-361
[55]. https://www.sciencedirect.com/science/article/pii/S0032063323000788
[56]. https://amostech.com/TechnicalPapers/2022/Poster/Koblick_2.pdf
[57]. https://www.researchgate.net/figure/Object-Detection-Model-Evaluation-Metrics_tbl1_373899578
[58]. https://spj.science.org/doi/10.34133/2022/9802793
[59]. https://ui.adsabs.harvard.edu/abs/2024amos.conf..101K/abstract
[61]. https://amostech.com/TechnicalPapers/2023/Cislunar-SSA/Griggs.pdf
[62]. https://www.nasa.gov/smallsat-institute/sst-soa/ground-data-systems-and-mission-operations/
[63]. https://amostech.com/TechnicalPapers/2022/Cislunar-SSA/Fahrner.pdf