This paper presents a novel approach to keeping the Random Finite Set (RFS) based Bayesian recursions tractable. We propose a randomized scheme using a Markov Chain Monte Carlo (MCMC) based technique and finite set statistics (FISST), termed Randomized FISST (R-FISST). This technique samples highly probable association hypotheses and uses them to approximate the posterior RFS based multi-object probability density function (pdf). It samples hypotheses without enforcing a heuristic number of samples so the number of samples is able to adjust naturally to the ambiguity of the data association problem (DAP). This provides the technique with a level of robustness to false associations. This is illustrated using two space situational awareness (SSA) examples. In the first example we compare the R-FISST technique to the Global Nearest Neighbor (GNN) technique. GNN represents the extreme case where the tracking problem is kept tractable, but the approximation of the multi-object pdf lacks association diversity, which leads to divergence in cardinality. The second example shows the scalability of the R-FISST technique by demonstrating its performance on collisional cascading, i.e., a cascading space object fragmentation event.
Application of a Randomized-Finite Set Statistics Technique (R-FISST) to Space Situational Awareness
J Astronaut Sci
The Journal of the Astronautical Sciences ; 69 , 4 ; 1149-1178
2022-08-01
30 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Space situational awareness , Collisional cascading , Space traffic management , Multiple object tracking , Markov Chain Monte Carlo , Finite set statistics Engineering , Aerospace Technology and Astronautics , Mathematical Applications in the Physical Sciences , Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics)
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