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Proceedings Paper

An improved multi-targets tracking algorithm based on cubature information particle filter
Author(s): Yihuan Zhao; Linlin Li; Qinghai Ding; Zhe Liu
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Paper 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.

Paper Details

Date Published: 15 November 2017
PDF: 6 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106051L (15 November 2017); doi: 10.1117/12.2291514
Show Author Affiliations
Yihuan Zhao, Space Star Technology Co., Ltd. (China)
Linlin Li, Space Star Technology Co., Ltd. (China)
Qinghai Ding, Space Star Technology Co., Ltd. (China)
Zhe Liu, Beihang Univ. (China)

Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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