Emily Gerber, The Stratagem Group, Inc; Thomas Kelecy, The Stratagem Group; Bill DeLude, The Stratagem Group; Bill McClintock, The Stratagem Group
Keywords: RSO Track Custody, Multi-State Filter, RSO Maneuver Detection
Abstract:
One of the key enablers for maintaining custody of active Resident Space Objects (RSOs) is persistent coverage which is realized by using tracking observations from geographically dispersed sensors. Adequate coverage that limits surveillance gaps ensures that any RSO maneuver can be detected and tracked in a timely fashion, though this is not sufficient criteria for maintaining custody. Coordination of multiple sensor systems must ensure consistent and calibrated tracking data, since biased or inconsistent measurements will limit overall tracking. A process was developed to provide consistent data quality assessment for an operational constellation of space-based Electro-Optical (EO) sensors. Using a multi-state Unscented Schmidt Kalman Filter (USKF), the process leverages the consolidated measurement and state information to provide a more robust and timely data quality assessment without the need for calibration satellites to be tasked and tracked. This processing paradigm also addresses an additional challenge in the orbit estimation filter: how does one distinguish between a sensor data anomaly and an RSO maneuver given the filter output metrics, like the pre- and post-fit measurement residuals and McReynolds filter-smoother consistency test?
The purpose of this work is to assess custody performance in the presence of both data anomalies and RSO maneuvers using representative observation cadences for a space-based constellation of EO sensors and processed using the multi-state USKF. Representative observations include appropriate applications of lighting and detection constraints, tracking cadences, and revisit times that are derived from operational systems. The sensors include both Geosynchronous Earth Orbit (GEO) and Low Earth Orbit (LEO) systems tracking GEO objects. Different RSO maneuver types and magnitudes will be modelled and evaluated for custody maintenance as a part of this study. Detection and estimation of sensor state errors are also examined with various types of data quality anomalies. The results demonstrate the ability to autonomously and reliably distinguish between data anomalies, sensor state errors, and RSO maneuvers in addition to maintaining custody of the maneuvering RSO. This implementation will lead to a more reliable assessment of data quality anomalies and RSO custody in the presence of maneuvers.
Date of Conference: September 27-20, 2022
Track: Astrodynamics