Mutual Application of Joint Probabilistic Data Association, Filtering, and Smoothing Techniques for Robust Multiple Space Object Tracking (Invited)

The Constrained Admissible Region-Multiple Hypothesis Filter (CAR-MHF) has previously been successfully applied to the problem of determining initial state and parameter estimates for non-operational space objects in the near-geosynchronous Earth orbit (GEO) regime. The application of CAR-MHF to a dense population of synthetically-created uncorrelated tracks (UCT) has been primarily challenged by two characteristics of the current implementation. First, the rapid uncertainty growth due to the noise characteristics of processing short-arc optical tracklets leads to ambiguous uncertainty overlap and the prevalence of mis-associated observations, primarily with a large statistical association gate. Second, the reliance on measurement update decisions made on a frame-by-frame basis without a process that evaluates the track estimate based upon all available associated data has prevented additional data from resolving these ambiguous conditions (i.e. if data from another object is statistically valid at a given instant of large ambiguity, an update will be performed and usually drives the filter to diverge). The work addressed in this paper addresses these challenges by harmoniously applying joint probabilistic data association in concert with backward smoothing. This is coupled with the McReynolds Filter/Smoother consistency checks to further minimize mis-associations. These methodologies are implemented to reduce the impacts of early ambiguity by sharing information across track updates and delaying hard decisions on track formation until sufficient data have been processed. Through filter/smoother assessment against both sparse and dense synthetically-created GEO optical data, the utility of mutually applying these two distinct techniques within the construct of the CAR-MHF algorithm for robust multiple space object tracking is be demonstrated.