Track-before-detect labeled multi-bernoulli particle filter with label switching

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.