Improved Particle Implementation of the Probability Hypothesis Density Filter in Resampling

Author

Tang, Xu ; Zhou, Jian ; Huang, Jian ; Wei, Ping

Author_Institution

Dept. of Electron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

fYear

2012

fDate

27-29 Oct. 2012

Firstpage

56

Lastpage

61

Abstract

A novel particle-PHD filter algorithm is proposed to deal with the multi-target tracking. It takes into account the most recent measurements by the unscented Kalman filter, not in the step of proposal distribution generation as usual, but in resampling step, to enhance the efficiency of the particle sampling. Simulation results show that the proposed algorithm outperforms the algorithms in the literature in performance but with extremely less computational cost.

Keywords

Kalman filters; particle filtering (numerical methods); probability; sampling methods; target tracking; improved particle implementation; multitarget tracking; novel particle-PHD filter algorithm; particle sampling; probability hypothesis density filter; resampling step; unscented Kalman filter; Approximation algorithms; Atmospheric measurements; Clutter; Filtering algorithms; Information filters; Particle measurements; Clustering; PHD Filter; Particle Filter; UKF; resampling;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Information Technology (CIT), 2012 IEEE 12th International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4673-4873-7

Type

conf

DOI

10.1109/CIT.2012.36

Filename

6391874