A Particle PHD Filter for Multi-Sensor Multi-Target Tracking Based on Sequential Fusion

Author

Meng Fanbin ; Hao Yanling ; Xia Quanxi ; OuYang Taishan ; Zou Wei

Author_Institution

Coll. of Autom., Harbin Eng. Univ., Harbin, China

fYear

2009

fDate

19-20 Dec. 2009

Firstpage

1

Lastpage

5

Abstract

This paper presents a sequential filter implementation of particle Probability Hypothesis Density (PHD) filter for multisensor multi-target tracking. The tracking system involves potentially nonlinear target dynamics described by Markov state space model and nonlinear measurements. Each sensor reports measurements to the tracking system, which performs sequential estimation of the current state using the particle PHD filter, which propagates only the first order statistical moment of the full posterior of the multi-target state. Simulation results are also given and compared with a single radar multi-target tracking, showing the advantage of the fusion tracking over the single radar multi-target tracking.

Keywords

Markov processes; filtering theory; sensor fusion; statistical analysis; target tracking; Markov state space model; first order statistical moment; fusion tracking; multisensor multitarget tracking; nonlinear measurements; particle PHD filter; probability hypothesis density filter; sequential filter; sequential fusion; Current measurement; Filters; Nonlinear dynamical systems; Particle measurements; Particle tracking; Performance evaluation; Radar tracking; Sensor systems; State-space methods; Target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4994-1

Type

conf

DOI

10.1109/ICIECS.2009.5366354

Filename

5366354