A robust tracking algorithm for surface targets

Tianzhi Yu; Yunhua Guo; Qiankun Kuang; Junmin Mou

doi:10.1117/12.3033568

13 June 2024 A robust tracking algorithm for surface targets

Tianzhi Yu, Yunhua Guo, Qiankun Kuang, Junmin Mou

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131804E (2024) https://doi.org/10.1117/12.3033568
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

This paper focuses on the issue of reduced estimation accuracy in target tracking systems due to the presence of varying levels of glint noise in radar measurements. Specifically, the performance degradation of the widely used the Huber Converted Measurement Kalman Filter (HCMKF) algorithm is addressed as the pollution rate of glint noise in measurements increases. In this paper, the Robust Converted Measurement Kalman Filter (RCMKF) algorithm based on approximate least absolute deviation is proposed to address this issue. Simulation results indicate that under measurements with lower pollution rate of glint noise, this method performs slightly better than HCMKF, and under measurements with higher pollution rate of glint noise, it outperforms HCMKF significantly. Moreover, the proposed algorithm has a lower computational cost compared to HCMKF.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tianzhi Yu, Yunhua Guo, Qiankun Kuang, and Junmin Mou "A robust tracking algorithm for surface targets", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131804E (13 June 2024); https://doi.org/10.1117/12.3033568

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