Sequential Monte Carlo methods for multiple target tracking and data fusion

被引：250

作者：

Hue, C ^{[1
]}

Le Cadre, JP

Pérez, P

机构：

[1] Univ Rennes 1, Irisa, Rennes, France

[2] CNRS, Irisa, Rennes, France

[3] Microsoft Res, Cambridge, England

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2002年 / 50卷 / 02期

关键词：

Bayesian estimation; bearings-only tracking; Gibbs sampler; multiple receivers; multiple targets tracking; particle filter;

D O I：

10.1109/78.978386

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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, e then investigate how to join passive and active measurements for improved tracking.

引用

页码：309 / 325

页数：17

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