Phillip Schmedeman, US Space Command; Joseph Gerber, Proxima Aerospace; Daniel Herber, Colorado State University
Keywords: Space Domain Awareness, satellite tracking, data analytics, evaluation framework, data-driven decision making
Abstract:
Our ability to track and characterize objects orbiting Earth has become essential for maintaining safety and security in space. Space Domain Awareness (SDA) plays a crucial role in understanding the operational environment and supporting decision-making processes for military, commercial, and civil space operators. Traditionally, the U.S. Department of Defense (DoD) has relied on the Space Surveillance Network (SSN), a system of ground- and space-based sensors, to detect, track, and catalog artificial objects in Earth’s orbit. While the SSN provides high-quality, secure, and reliable data, it faces challenges in scalability, adaptability, and innovation. The rapid commercialization of space has introduced an alternative source of SDA data—privately operated ground-based sensors. These commercial systems offer a cost-effective means of augmenting traditional capabilities by employing diverse sensing technologies and providing global coverage. However, integrating commercial SDA data presents a trade-off between cost and accuracy. Variability in sensor quality, revisit rates, and data precision necessitates a structured framework for evaluating commercial contributions to mission effectiveness.
The proliferation of commercial SDA providers presents both opportunities and challenges. On the one hand, commercial sensors expand observational capacity and introduce technological advancements at a faster rate than government systems. On the other hand, the growing number of providers, sensor modalities, and data streams increases the risk of coverage gaps, unintentional redundancy, and data inconsistencies. Without an evaluation framework, U.S. Space Command and its subordinate organizations, such as the National Space Defense Center (NSDC) and the Joint Commercial Operations (JCO), face difficulties in making informed purchasing decisions and optimizing sensor utilization.
This research proposes an operations research framework to quantify the value of SDA data acquired from commercial ground-based sensor providers. Specifically, we focus on evaluating the effectiveness of commercial data in detecting and verifying on-orbit maneuvers, a key aspect of SDA. Maneuvers—deliberate changes in a satellite’s trajectory—can have significant implications for space security, collision avoidance, and operational planning. The ability to detect maneuvers in a timely and reliable manner is critical for ensuring the integrity of space operations. Additionally, the integration of data from multiple sensor types introduces the potential for sensor fusion, wherein combining observations from electro-optical, radar, and time-difference-of-arrival (TDOA) sensors can enhance accuracy, reduce false positives, and improve maneuver detection reliability.
To achieve this, we introduce a suite of quantitative metrics and visualizations designed to assess the performance of commercial SDA providers. These metrics include revisit rate, violation time (i.e., time exceeding required observation intervals), observation persistence, and mean trace of covariance for state vectors. Revisit rate and violation time provide insight into the frequency and reliability of satellite observations, ensuring that high-interest objects remain under continuous monitoring. Observation persistence measures the consistency of commercial sensor coverage, while covariance analysis helps evaluate the precision of state vector data and detect potential maneuvers. By applying these metrics to anonymized commercial SDA data, we illustrate how they can be used to balance cost-effectiveness with data accuracy, ensuring that commercial contributions align with mission objectives.
Our research also explores the utility of maneuver verification techniques using state vector data. While early warning of maneuvers ideally comes from direct observations, state vector analysis provides a retrospective means of assessing whether a maneuver has occurred. By tracking fluctuations in covariance values over time, we establish an independent approach for identifying potential maneuvers, supplementing existing commercial maneuver reporting. This methodology enables Space Command to validate external maneuver reports and develop an improved understanding of provider capabilities.
Additionally, this study considers the broader implications of commercial SDA integration within DoD operations. The Unified Data Library (UDL) serves as a central repository for SDA data, facilitating the aggregation and dissemination of observations, state vectors, and maneuver reports from both military and commercial sources. Our research examines how JCO’s high-rate revisit (HRR) prioritization system aligns with commercial provider performance and how well commercial data meets mission-driven objectives. By evaluating the contributions of individual providers and sensor types, we aim to inform data purchasing decisions and improve overall SDA effectiveness.
A secondary objective of this research is to enhance collaboration between the DoD and commercial SDA providers. By developing transparent and quantifiable performance metrics, we provide a mechanism for constructive feedback to commercial entities, encouraging improvements in data quality, consistency, and responsiveness. This feedback loop fosters a mutually beneficial relationship in which the DoD gains access to enhanced SDA capabilities while commercial providers refine their services to better meet operational needs.
Ultimately, this study contributes to the ongoing evolution of SDA by providing a data-driven framework for optimizing sensor utilization and maneuver detection. The findings support Space Command’s efforts to integrate commercial SDA data into existing workflows, ensuring that decision-makers have access to timely and actionable information. As the space environment becomes increasingly congested and contested, the ability to dynamically assess and prioritize sensor data will be essential for maintaining strategic advantage in the domain.
While this research lays the groundwork for improved SDA data assessment, future work will focus on refining maneuver detection models, incorporating machine learning techniques, and expanding the evaluation framework to include space-based commercial sensors. Further investigation into multi-sensor fusion techniques may provide additional improvements in accuracy while maintaining cost-effectiveness, supporting a sustainable approach to SDA operations. The continued development of quantitative SDA metrics will play a crucial role in shaping the future of space operations, enabling both military and commercial stakeholders to navigate the challenges of an evolving orbital environment.
Date of Conference: September 16-19, 2025
Track: Space Domain Awareness