Multi-target state estimation and track continuity for the particle PHD filter

Author

Clark, Daniel E. ; Bell, Judith

Author_Institution

Heriot-Watt Univ., Edinburgh

Volume

43

Issue

4

fYear

2007

fDate

10/1/2007 12:00:00 AM

Firstpage

1441

Lastpage

1453

Abstract

Particle filter approaches for approximating the first-order moment of a joint, or probability hypothesis density (PHD), have demonstrated a feasible suboptimal method for tracking a time-varying number of targets in real-time. We consider two techniques for estimating the target states at each iteration, namely k-means clustering and mixture modelling via the expectation-maximization (EM) algorithm. We present novel techniques for associating the targets between frames to enable track continuity.

Keywords

expectation-maximisation algorithm; filtering theory; pattern clustering; probability; target tracking; tracking filters; expectation-maximization algorithm; iteration; k-means clustering; multiple target filtering; multiple target tracking; multitarget probability distribution; multitarget state estimation; particle PHD filter; probability hypothesis density; Bayesian methods; Clustering algorithms; Filtering; Particle filters; Particle measurements; Particle tracking; Probability distribution; Radar tracking; State estimation; Target tracking;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/TAES.2007.4441750

Filename

4441750