Parallel Particle Filters for Multiple Target Tracking
Sebbagh Abdennour and Tebbikh
Hicham
Automatic
and Computing Laboratory of Guelma (LAIG), 8 Mai 1945 Guelma University,
Algeria
Abstract: The Multiple Targets Tracking (MTT) problem
is addressed in signal and image processing. When the state and measurement models are linear, we
can find several algorithms that yield good performances in MTT problem, among
them, the Multiple Hypotheses Tracker (MHT) and the Joint Probabilistic Data
Association Filter (JPDAF). However, if the state and measurement models are
nonlinear, these algorithms break down. In this paper we
propose a method based on particle filters bank, where the objective is to make
a contribution for estimating the trajectories of several targets using only
bearings measurements. The main idea of this algorithm is to combine the Multiple
Model approach (MM) with Sequential Monte Carlo methods (SMC). The result from
this combination is a Nonlinear Multiple Model Particle Filters algorithm
(NMMPF) able to estimate the trajectories of multiple targets.
Keywords: MM approach, MTT, particle filtering.
Received August 21, 2014; accepted December 21, 2014; Published online December 23, 2015