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

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) algorit...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2019-07, Vol.21 (8), p.740
Main Authors: Li, Xiaohua, Zhao, Chenxu, Yu, Jing, Wei, Wei
Format: Article
Language:English
Subjects:
Algorithms
Bayesian theory
Bearing (direction)
bearing-Doppler
bearing-only
Covariance matrix
Extended Kalman filter
Linear algebra
Noise
observability
Random variables
Sensors
square root unscented Kalman filter
target tracking
Tracking problem
Tracking systems
Underwater
Velocity
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Summary: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.
ISSN:1099-4300
1099-4300
DOI:10.3390/e21080740