Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 56, Issue 4, Pages 1313-1326Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2007.908968
Keywords
Byesian filtering; CPHD filter; Gaussian sum filter; Kalman filter; particle filter; PHD filter; point processes; random finite sets; target tracking
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This paper presents a novel and mathematically rigorous Bayes' recursion for tracking a target that generates multiple measurements with state dependent sensor field of view and clutter. Our Bayesian formulation is mathematically well-founded due to your use of a consistent likelihood function derived from random finite set theory. It is established that under certain assumptions, the proposed Bayes' recursion reduces to the cardinalized probability hypothesis density (CPHD) recursion for a single target. A particle implementation of the proposed recursion is given. Under linear Gaussian and constant sensor field of view assumptionsm exact closed-form solution to the proposed recursion is derive, and efficient implementations are given. Extensions of the prosed-form recursion to accommodate mild nonlinearities are also given using linearization and unscented transforms.
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