An overview of particle methods for random finite set models

• Stochastic nonlinear filtering using particle filters (PFs).
• Random finite set models dramatically widened the scope of applications of PFs.
• Covers the Bernoulli PF, the PHD-PF and the generalised labelled multi-Bernoulli PF.
• Performance demonstrated in the context of bearings-only target tracking.

This overview paper describes the particle methods developed for the implementation of the 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.