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Open AccessArticle

Underwater Bearing-Only and Bearing-Doppler Target Tracking Based on Square Root Unscented Kalman Filter

by Xiaohua Li 1,*, Chenxu Zhao 2, Jing Yu 3 and Wei Wei 1
1
Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
2
Science and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology, Changsha 741200, China
3
School of Marine Engineering Northwestern Polytechnical University, Xi’an 710072, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(8), 740; https://doi.org/10.3390/e21080740
Received: 26 June 2019 / Revised: 24 July 2019 / Accepted: 27 July 2019 / Published: 28 July 2019
(This article belongs to the Special Issue Entropy and Information Theory in Acoustics)
Underwater target tracking system can be kept covert using the bearing-only or the bearing-Doppler measurements (passive measurements), which will reduce the risk of been detected. According to the characteristics of underwater target tracking, the square root unscented Kalman filter (SRUKF) algorithm, which is based on the Bayesian theory, was applied to the underwater bearing-only and bearing-Doppler non-maneuverable target tracking problem. Aiming at the shortcomings of the unscented Kalman filter (UKF), the SRUKF uses the QR decomposition and the Cholesky factor updating, in order to avoid that the process noise covariance matrix loses its positive definiteness during the target tracking period. The SRUKF uses sigma sampling to avoid the linearization of the nonlinear bearing-only and the bearing-Doppler measurements. To ensure the target state observability in underwater target tracking, the paper uses single maneuvering observer to track the single non-maneuverable target. The simulation results show that the SRUKF has better tracking performance than the extended Kalman filter (EKF) and the UKF in tracking accuracy and stability, and the computational complexity of the SRUKF algorithm is low. View Full-Text
Keywords: underwater; bearing-only; bearing-Doppler; square root unscented Kalman filter; observability; target tracking; Bayesian theory underwater; bearing-only; bearing-Doppler; square root unscented Kalman filter; observability; target tracking; Bayesian theory
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Li, X.; Zhao, C.; Yu, J.; Wei, W. Underwater Bearing-Only and Bearing-Doppler Target Tracking Based on Square Root Unscented Kalman Filter. Entropy 2019, 21, 740.

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