Joint probabilistic data association and smoothing applied to multiple space object tracking

A foundational aspect of space domain awareness is the ability to identify and track space objects, including space object discovery and custody. This paper demonstrates the power of combining an efficient multiple hypothesis joint probabilistic data association (MH-JPDA) algorithm with a fixed-interval smoother to simultaneously track multiple space objects. For newly discovered objects, statistical initial orbit determination (SIOD) is possible with a single short optical tracklet, but results in large initial uncertainties. Combining these uncertainties with closely spaced objects can result in highly ambiguous data associations, which can lead to poor state estimates and even filter divergence. This paper invokes MH-JPDA to probabilistically update multiple tracks with multiple simultaneous observations in a sequential filter, while avoiding assigning one-to-one associations. Once sufficient information has been collected, the space objects become uniquely distinguishable among each other. Subsequently, the smoother is applied to achieve improved association of the prior observations. MH-JPDA allows for immediate track formation (using SIOD) and sequential processing of incoming observations, providing statistically rigorous real-time state estimates, whereas smoothing produces a more-refined, higher-confidence overall track estimate at user-defined intervals. This paper demonstrates this approach within the Constrained Admissible Region, Multiple Hypothesis Filter (CAR-MHF) software by tracking a simulated break-up scenario.Jason Stauch, Travis Bessell, Mark Rutten, Jason Baldwin, Moriba Jah and Keric Hil