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Remote Sensing
Volume 13
Issue 15
10.3390/rs13152915
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Article

Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter

by
Zihao Huang
1,2,
Shijin Chen
1,2,
Chengpeng Hao
1,* and
Danilo Orlando
3
1
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2
School of Electronic, Electrical and Communication, University of Chinese Academy of Sciences, Beijing 100864, China
3
Department of Engineering, University of Niccolo Cusano, via Don Carlo Gnocchi 3, 00166 Rome, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(15), 2915; https://doi.org/10.3390/rs13152915
Submission received: 18 May 2021 / Revised: 9 July 2021 / Accepted: 10 July 2021 / Published: 24 July 2021
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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Abstract

In bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) attracts much attention because of its stability and its low computational burden. However, the PLKF’s measurement vector and the pseudo-linear noise are correlated, which makes it suffer from bias problems. Although the bias-compensated PLKF (BC–PLKF) and the instrumental variable-based PLKF (IV–PLKF) can eliminate the bias, they only work well when the target behaves with non-manoeuvring movement. To extend the PLKF to the manoeuvring target tracking scenario, an unbiased PLKF (UB–PLKF) algorithm, which splits the noise away from the measurement vector directly, is proposed. Based on the results of the UB–PLKF, we also propose its velocity-constrained version (VC–PLKF) to further improve the performance. Simulations show that the UB–PLKF and VC–PLKF outperform the BC–PLKF and IV–PLKF both in non-manoeuvring and manoeuvring scenarios.
Keywords: bearings-only tracking; pseudo-linear Kalman filter; norm-constrained Kalman filter bearings-only tracking; pseudo-linear Kalman filter; norm-constrained Kalman filter

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MDPI and ACS Style

Huang, Z.; Chen, S.; Hao, C.; Orlando, D. Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter. Remote Sens. 2021, 13, 2915. https://doi.org/10.3390/rs13152915

AMA Style

Huang Z, Chen S, Hao C, Orlando D. Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter. Remote Sensing. 2021; 13(15):2915. https://doi.org/10.3390/rs13152915

Chicago/Turabian Style

Huang, Zihao, Shijin Chen, Chengpeng Hao, and Danilo Orlando. 2021. "Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter" Remote Sensing 13, no. 15: 2915. https://doi.org/10.3390/rs13152915

APA Style

Huang, Z., Chen, S., Hao, C., & Orlando, D. (2021). Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter. Remote Sensing, 13(15), 2915. https://doi.org/10.3390/rs13152915

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