An improved multi-targets tracking algorithm based on cubature information particle filter

Yihuan Zhao; Linlin Li; Qinghai Ding; Zhe Liu

doi:10.1117/12.2291514

15 November 2017 An improved multi-targets tracking algorithm based on cubature information particle filter

Yihuan Zhao, Linlin Li, Qinghai Ding, Zhe Liu

Author Affiliations +

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106051L (2017) https://doi.org/10.1117/12.2291514
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

Abstract

In the traditional bootstrap particle filter, the state transition density is used as the importance sampling function, which brings some problems such as particle degradation and poor tracking accuracy. In this paper, the posterior probability is used as the importance sampling function and its estimation method is proposed. By means of cubature information filtering and Gating technique, the mean and variance of the importance sampling function are estimated, and the importance sampling function is designed. The improved particle filter method is used to estimate the number of targets and the number of targets in the nonlinear situation. The simulation results show that the proposed algorithm has the advantages of high estimation accuracy and good stability in the nonlinear multi-target tracking scenario.

Citation Download Citation

Yihuan Zhao, Linlin Li, Qinghai Ding, and Zhe Liu "An improved multi-targets tracking algorithm based on cubature information particle filter", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106051L (15 November 2017); https://doi.org/10.1117/12.2291514

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE