Poisson Multi-Bernoulli Mixture Filter With General Target-Generated Measurements and Arbitrary Clutter

This paper shows that the Poisson multi-Bernoulli mixture (PMBM) density is a multi-target conjugate prior for general target-generated measurement distributions and arbitrary clutter distributions. That is, this model standard dynamic with birth model, predicted filtering densities are PMBMs. We derive corresponding PMBM recursion. Based on result, we implement filter point-target models negative binomial in which data association hypotheses high weights chosen via Gibbs sampling. also an extended target union of Poisson-distributed finite number independent sources. Simulation results show benefits proposed filters to deal non-standard clutter.