The (MR)MTPF: particle filters to track multiple targets using multiple receivers
Carine Hue, Jean–Pierre Le Cadre, Patrick Pérez · 2001
The classical particle filter deals with the estimation of one state process conditionally to a realization of one observation process. We extend it here to the estimation of multiple state processes given realizations of several kinds of observation processes. The new algorithm is used to track with success multiple targets in a bearingsonly context. Making use of its abilities to mix different types of observations, we then investigate how to join passive and active measurements to improve tracking results. Keywords: Particle filter, Bayesian estimation, Gibbs sampler, multiple targets tracking, multiple receivers, bearings-only tracking . 1