Abstract
Accurate 3D passive tracking of an underwater uncooperative target is of great significance to make use of the sea resources as well as to ensure the safety of our maritime areas. In this paper, a 3D passive underwater uncooperative target tracking problem for a time-varying non-Gaussian environment is studied. Aiming to overcome the low observability drawback inherent in the passive target tracking problem, a distributed passive underwater buoys observing system is considered and the optimal topology of the distributed measurement system is designed based on the nonlinear system observability analysis theory and the Cramer–Rao lower bound (CRLB) analysis method. Then, considering the unknown underwater environment will lead to time-varying non-Gaussian disturbances for both the target’s dynamics and the measurements, the robust optimal nonlinear estimator, namely the adaptive particle filter (APF), is proposed. Based on the Bayesian posterior probability and Monte Carlo techniques, the proposed algorithm utilizes the real-time optimal estimation technique to calculate the complex noise online and tackle the underwater uncooperative target tracking problem. Finally, the proposed algorithm is tested by simulated data and comprehensive comparisons along with detailed discussions that are made to demonstrate the effectiveness of the proposed APF.
Highlights
The marine resources have significant influences on human’s living and social developments
Considering the large sea area to be monitored and the increasingly intelligent underwater target in the research area of underwater target tracking, the high safety coefficient and low energy consumption advanced passive tracking techniques are of great importance to be researched
For the underwater target tracking scenario, the unknown underwater environment will probably influence the kinematics of the target and the measurements made by every underwater buoy so that the noise can be time-varying and non-Gaussian
Summary
The marine resources have significant influences on human’s living and social developments. For the underwater target tracking scenario, the unknown underwater environment will probably influence the kinematics of the target and the measurements made by every underwater buoy so that the noise can be time-varying and non-Gaussian Considering this assumption, only a limited number of particle filter (PF)-based researches that utilize the Bayesian posterior estimation method to overcome the Gaussian white noise limitation are designed [23,24], and very few researchers pay attention to the time-varying characteristic of the underwater noise. Considering all the above-mentioned challenges and in order to track the underwater uncooperative target in a 3D real-time scenario under the time-varying non-Gaussian environment by distributed passive submerged buoys, this paper firstly proposed a real-time optimal topology-forming method of the distributed measurement system that balances the tracking robustness and accuracy.
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