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This study is concerned with multi-target tracking (MTT). The Cramér-Rao lower bound (CRB) is the basic tool for investigating estimation performance. Though basically defined for estimation of deterministic parameters, it has been extended to stochastic ones in a Bayesian setting. In the target tracking area, we have thus to deal with the estimation of the whole trajectory, itself described by a Markovian model. This leads up to the recursive formulation of the posterior CRB (PCRB). The aim of the work presented here is to extend this calculation of the PCRB to MTT under various assumptions. [ABSTRACT FROM AUTHOR]

Investigates multitarget tracking (MTT) in the framework of particle filtering and Gibbs sampling. Description of the basic particle filter; Application to bearings-only problems; Extension of particle filtering to MTT.