  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.
