An Overview of Particle Methods for Random Finite Set Models

TL;DR

This paper reviews particle methods used in random finite set models, focusing on filters like Bernoulli, PHD, and GLMB, and demonstrates their effectiveness in bearings-only target tracking.

Contribution

It provides a comprehensive overview of particle methods for RFS-based Bayes filters, highlighting their implementation and performance in target tracking.

Findings

Effective in bearings-only target tracking

Demonstrates the performance of Bernoulli, PHD, and GLMB filters

Provides implementation insights for RFS particle methods

Abstract

This overview paper describes the particle methods developed for the implementation of the a class of Bayes filters formulated using the random finite set formalism. It is primarily intended for the readership already familiar with the particle methods in the context of the standard Bayes filter. The focus in on the Bernoulli particle filter, the probability hypothesis density (PHD) particle filter and the generalised labelled multi-Bernoulli (GLMB) particle filter. The performance of the described filters is demonstrated in the context of bearings-only target tracking application.