The increasing number of anthropogenic space objects (ASOs) in low Earth orbit (LEO) poses a threat to the safety and sustainability of the space environment. Multiple companies are planning to launch large constellations of hundreds to thousands of satellites in the near future, increasing the probability of collisions and debris generation. This paper analyzes the long-term evolution of the LEO ASO population with the goal of estimating LEO orbital capacity. This is carried out by introducing a new probabilistic source–sink model. The developed source–sink model is a multishell multispecies model, which includes different object species, such as active and derelict satellites, and debris. Furthermore, debris are divided into the following two subgroups: trackable and nontrackable debris, the last ones representing a significant hazard for active satellites. In addition, the proposed model accounts for collision events and atmospheric drag effects, which include the influence of solar activity. Indeed, the Jacchia–Bowman 2008 thermospheric density model is exploited. The results prove that considering untracked debris within the model produces more collisions, and therefore a smaller population of active satellites affecting the safety of LEO and its orbital capacity.

To maintain a robust catalog of resident space objects (RSOs), space situational awareness (SSA) mission operators depend on ground- and space-based sensors to repeatedly detect, characterize, and track objects in orbit. Although some space sensors are capable of monitoring large swaths of the sky with wide fields of view (FOVs), others—such as maneuverable optical telescopes, narrow-band imaging radars, or satellite laser-ranging systems—are restricted to relatively narrow FOVs and must slew at a finite rate from object to object during observation. Since there are many objects that a narrow FOV sensor could choose to observe within its field of regard (FOR), it must schedule its pointing direction and duration using some algorithm. This combinatorial optimization problem is known as the sensor-tasking problem. In this paper, we developed a deep reinforcement learning agent to task a space-based narrow-FOV sensor in low Earth orbit (LEO) using the proximal policy optimization algorithm. The sensor’s performance—both as a singular sensor acting alone, but also as a complement to a network of taskable, narrow-FOV ground-based sensors—is compared to the greedy scheduler across several figures of merit, including the cumulative number of RSOs observed and the mean trace of the covariance matrix of all of the observable objects in the scenario. The results of several simulations are presented and discussed. Additionally, the results from an LEO SSA sensor in different orbits are evaluated and discussed, as well as various combinations of space-based sensors.

Space situational awareness (SSA) is becoming increasingly challenging with the proliferation of resident space objects (RSOs), ranging from CubeSats to mega-constellations. Sensors within the United States Space Surveillance Network are tasked to repeatedly detect, characterize, and track these RSOs to retain custody and estimate their attitude. The majority of these sensors consist of ground-based sensors with a narrow field of view and must be slew at a finite rate from one RSO to another during observations. This results in a complex combinatorial problem that poses a major obstacle to the SSA sensor tasking problem. In this work, we successfully applied deep reinforcement learning (DRL) to overcome the curse of dimensionality and optimally task a ground-based sensor. We trained several DRL agents using proximal policy optimization and population-based training in a simulated SSA environment. The DRL agents outperformed myopic policies in both objective metrics of RSOs’ state uncertainties and the number of unique RSOs observed over a 90-min observation window. The agents’ robustness to changes in RSO orbital regimes, observation window length, observer’s location, and sensor properties are also examined. The robustness of the DRL agents allows them to be applied to any arbitrary locations and scenarios.

A significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Neutral mass density variability in the thermosphere, driven by enhanced geomagnetic activity and solar EUV absorption, is a major source of satellite propagation error. This work investigates the impacts of space weather driver forecasting uncertainty on satellite drag and collision avoidance maneuver decision‐making. Since most operational space weather driver forecasts do not offer an uncertainty assessment, the satellite operator community is left to make dangerous assumptions about the trustworthiness of the forecast models they use to perform satellite state propagation. Climatological persistence‐based forecast models are developed for F_10.7 and Kp. These models accurately capture the heteroscedastic and, at times, highly non‐Gaussian uncertainty distribution on forecasts of the drivers of interest. A set of realistic satellite conjunction scenarios is simulated to demonstrate the contributions of space weather driver forecast uncertainty on the probability of collision and maneuver decisions. Improved driver forecasts, especially forecasts of F_10.7, are demonstrated to be very useful for enabling durable maneuver decisions with additional lead time (up to 24 hr for the period examined), though the improvement depends on the specific conjunction scenario of interest.

The sustainability of the low-Earth-orbit (LEO) environment is threatened by the growing number of anthropogenic space objects planned to be launched in the coming years. This paper investigates the evolution of objects residing in LEO through the MIT Orbital Capacity Assessment Tool (MOCAT), an evolutionary multishell, multispecies source-sink model. The proposed novelty considers the flow of objects crossing multiple shells during orbit raising and deorbiting maneuvers, modeled through the secular variation of the semimajor axis under a low-thrust continuous applied control. To this aim, a higher-fidelity MOCAT version, including active satellites, derelicts, debris, and rocket bodies, has been developed and used. The results demonstrate that incorporating orbit transfer fluxes into the model results in a higher number of collisions, which leads to a greater quantity of debris and poses a greater threat to the safety of LEO.

Despite the availability of extensive historical data on Earth-orbiting objects, artificial intelligence (AI) adoption in space domain awareness remains limited. To address this gap, the 2024 MIT ARCLab Prize for AI Innovation in Space challenged participants to develop AI models for characterizing satellite pattern-of-life (PoL) in Geostationary Earth Orbit. The challenge focused on developing machine learning models capable of classifying behavioral patterns and detecting key transition events in multivariate time-series data. The challenge dataset comprised of 2402 satellite trajectories spanning six months with a two-hour temporal resolution. The data are generated using high-fidelity satellite propagators based on simulated trajectories, Vector Covariance Message data, and two-line elements. This dataset features diverse operational behaviors and propulsion systems, providing a robust foundation for AI analysis. The challenge attracted over 100 teams worldwide, with more than 350 submissions showcasing a diverse range of AI approaches, including deep learning architectures (CNNs, LSTMs, transformers), gradient-boosting techniques (XGBoost, CatBoost), and hybrid models. The top performing teams demonstrated AI’s effectiveness in PoL characterization, with Hawaii2024 achieving an F2 score of 0.952 on the partial test set using a CNN-LSTM hybrid approach, followed closely by Millennial-IUP and QR_Is that utilized XGBoost with tailored transition-labeling and gradient-boosted decision tree with a model-stacking strategy, respectively. This paper presents an analysis of the competition’s dataset, evaluation methodology, and top-performing solutions.