15 November 2017 An improved multi-targets tracking algorithm based on cubature information particle filter
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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.
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Yihuan Zhao, Linlin Li, Qinghai Ding, 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); doi: 10.1117/12.2291514; https://doi.org/10.1117/12.2291514
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