Sequential Monte Carlo methods for multiple target tracking and data fusion

Author

Hue, Carine ; Le Cadre, Jean-Pierre ; Pérez, Patrick

Author_Institution

IRISA, Rennes I Univ., France

Volume

50

Issue

2

fYear

2002

fDate

2/1/2002 12:00:00 AM

Firstpage

309

Lastpage

325

Abstract

The classical particle filter deals with the estimation of one state process conditioned on 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 bearings-only context, whereas a JPDAF diverges. Making use of the ability of the particle filter to mix different types of observations, we then investigate how to join passive and active measurements for improved tracking

Keywords

Monte Carlo methods; filtering theory; sensor fusion; sequential estimation; target tracking; Bayesian estimation; active measurements; bearings-only tracking; classical particle filter; data fusion; multiple state processes; multiple target tracking; observation processes; passive measurements; sequential Monte Carlo methods; Data mining; Filtering; NP-hard problem; Nonlinear equations; Particle filters; Particle measurements; Particle tracking; Signal processing algorithms; State estimation; Target tracking;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.978386

Filename

978386