A Particle PHD Filter with Improved Resampling Design for Multiple Target Tracking

Author

Zeng Xiaohui ; Shi Yibing ; Lian Yi

Author_Institution

Sch. of Autom. Eng., UESTC, Chengdu, China

fYear

2014

fDate

19-21 Dec. 2014

Firstpage

1844

Lastpage

1849

Abstract

Multi-target tracking is a complex problem including time-varying number of targets and their states in the presence of data association uncertainty and clutter. In this article, we develop a novel implementation of Sequential Monte Carlo filter with a new improved partial resampling strategy in random finite sets framework. This algorithm provides an approach to increase diversity of particles and keep accuracy of filtering performance. Simulation results verify that for the MTT problems, the proposed algorithm could achieve better performance than the standard particle PHD filter.

Keywords

Monte Carlo methods; particle filtering (numerical methods); probability; signal sampling; target tracking; MTT problems; clutter; data association uncertainty; multitarget tracking; partial resampling strategy; particle PHD filter; random finite sets framework; sequential Monte Carlo filter; time-varying targets; Clutter; Filtering algorithms; Filtering theory; Monte Carlo methods; Particle filters; Standards; Target tracking; partial resampling; probability hypothesis density (PHD) filter; random finite sets; target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Science and Engineering (CSE), 2014 IEEE 17th International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4799-7980-6

Type

conf

DOI

10.1109/CSE.2014.338

Filename

7023849