DocumentCode
2113607
Title
Study of Multi-target Tracking and Data Association Based on Sequential Monte Carlo Algorithm
Author
Lin-bo, Fan ; Li, Kang ; Ying-cheng, Wu ; Ming, Zhao
Author_Institution
Inst. of Reliability Eng., Guizhou Univ., Guiyang
fYear
2008
fDate
18-18 Dec. 2008
Firstpage
134
Lastpage
139
Abstract
A new method based on sequential Monte Carlo algorithm is proposed for tracking multi-target and data association in non-linear system. The algorithm partitions the problem of multi-target tracking into two problems: single target tracking and data association. Single target tracking is implemented by using UKF and data association by using sequential Monte Carlo algorithm. Since Particle Filter has advantages in non-linear non-Gauss system, the proposed method performs well in the experiment.
Keywords
Kalman filters; Monte Carlo methods; particle filtering (numerical methods); sensor fusion; target tracking; UKF; data association; multitarget tracking; nonlinear nonGauss system; particle filter; sequential Monte Carlo algorithm; Monte Carlo methods; Nonlinear filters; Particle filters; Particle tracking; Partitioning algorithms; Reliability engineering; Seminars; State estimation; Target tracking; Taylor series; UKF; data association; particle filtering; target tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Future BioMedical Information Engineering, 2008. FBIE '08. International Seminar on
Conference_Location
Wuhan, Hubei
Print_ISBN
978-0-7695-3561-6
Type
conf
DOI
10.1109/FBIE.2008.73
Filename
5076703
Link To Document