This paper presents a multitarget tracking particle filter for general track-before-detect measurement models. The particle filter is presented in the random-finite-set framework and uses a labeled multi-Bernoulli approximation. I also present a label-switching improvement algorithm based on Markov-chain Monte Carlo methods that is expected to increase filter performance if targets are in close proximity for a sufficiently long time. The particle filter is tested in two challenging numerical examples.
Track-before-detect labeled multi-bernoulli particle filter with label switching
IEEE Transactions on Aerospace and Electronic Systems ; 52 , 5 ; 2123-2138
2016-10-01
1272874 byte
Article (Journal)
Electronic Resource
English
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