A New Particle Filter for Target Tracking

Author

Yali, Wang

Author_Institution

East China Univ. of Sci. & Technol., Shanghai

fYear

2008

fDate

21-22 Dec. 2008

Firstpage

593

Lastpage

596

Abstract

In target tracking system, the measurement and the true state of target are nonlinear. Particle filter can deal with nonlinear and nonGaussian filtering. The crucial technique of particle filter is the selection of proposal function. In this paper is presented the particle filter-BLUE_PF, which is based on BLUE measurement conversion to get the proposal function. The state estimation and its covariance, which are derived from BLUE measurement conversion, can be used to determine the proposal function. The simulation results show that BLUE_PF has superiority to EKF_PF and UKF_PF.

Keywords

covariance matrices; nonlinear filters; particle filtering (numerical methods); state estimation; target tracking; tracking filters; BLUE measurement conversion; covariance matrix; nonGaussian filtering; nonlinear filtering; particle filter; state estimation; target tracking system; Bayesian methods; Coordinate measuring machines; Covariance matrix; Estimation error; Noise measurement; Particle filters; Particle measurements; Proposals; Radar tracking; Target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Technology Application Workshops, 2008. IITAW '08. International Symposium on

Conference_Location

Shanghai

Print_ISBN

978-0-7695-3505-0

Type

conf

DOI

10.1109/IITA.Workshops.2008.74

Filename

4732008