C. Frueh1
CMES-Computer Modeling in Engineering & Sciences, Vol.111, No.2, pp. 171-201, 2016, DOI:10.3970/cmes.2016.111.171
Abstract In recent years, probabilistic tracking methods have been becoming increasingly popular for solving the multi-target tracking problem in Space Situational Awareness (SSA). Bayesian frameworks have been used to describe the objects' of interest states and cardinality as point processes. The inputs of the Bayesian framework filters are a probabilistic description of the scene at hand, the probability of clutter during the observation, the probability of detection of the objects, the probability of object survival and birth rates, and in the state update, the measurement uncertainty and process noise for the propagation. However, in the filter derivation, the assumptions of Poisson… More >
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