A track-before-detect labelled multi-Bernoulli particle filter with label switching

TL;DR

This paper introduces a labelled multi-Bernoulli particle filter for multitarget track-before-detect, incorporating a label switching algorithm to improve performance during target proximity, validated through challenging numerical tests.

Contribution

It proposes a novel labelled multi-Bernoulli particle filter with a label switching algorithm for enhanced multitarget tracking in track-before-detect scenarios.

Findings

Improved tracking accuracy in close target scenarios.

Effective label switching algorithm based on MCMC.

Validated performance through challenging numerical examples.

Abstract

This paper presents a multitarget tracking particle filter (PF) for general track-before-detect measurement models. The PF is presented in the random finite set framework and uses a labelled multi-Bernoulli approximation. We also present a label switching improvement algorithm based on Markov chain Monte Carlo that is expected to increase filter performance if targets get in close proximity for a sufficiently long time. The PF is tested in two challenging numerical examples.