A new performance index for measuring the effect of single target tracking with Kalman particle filter

Xinbiao Lu, Fang Li (), Jing Tang (), Zhuxin Chai () and Mingyue Hu ()
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Xinbiao Lu: School of Energy and Electrical Engineering, Hohai University, Nanjing 211100, P. R. China
Fang Li: School of Energy and Electrical Engineering, Hohai University, Nanjing 211100, P. R. China
Jing Tang: ��Jiangsu Xin Electronics Company Limited, Suzhou 215137, P. R. China
Zhuxin Chai: ��Jiangsu Xin Electronics Company Limited, Suzhou 215137, P. R. China
Mingyue Hu: School of Energy and Electrical Engineering, Hohai University, Nanjing 211100, P. R. China

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 09, 1-14

Abstract: In dealing with the problems of nonlinear and non-Gaussian systems, the Kalman particle filter (KPF) algorithm has been widely used for the aerial and underwater target tracking system, which is vulnerable to environmental interference in recent years, especially in military fields. However, in most of the target tracking research, the indexes used by researchers to evaluate tracking algorithms are usually limited to the state difference or the root mean square error in the tracking process, and the overall tracking effect is only evaluated by the tracking time. These indexes cannot be evaluated for the algorithm that does not have obvious error optimization performance in the tracking process but actually improves the overall tracking effect. Therefore, this paper proposes to evaluate the tracking effect of different tracking algorithms by using the mean square error of relative points in the plane control network of engineering survey. Simulation results show that the proposed performance index combined with the tracking sampling time and the mean square error of tracking position can effectively evaluate the local and global tracking performance of different algorithms.

Keywords: Particle filter; Kalman particle filter; mean square error of relative points; target tracking (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0129183122501169

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