Wei Zhang, Naixin Wang, Shilin Wei, Jia Zeng, and Wenhua Wu
Target hunting, multi-AUVs, consensus theory, cross-EKF; BOT
A novel tracking method applying the consensus theory to solve the underwater moving target hunting problem is proposed in this article. By treating the moving target as a virtual member in the autonomous underwater vehicle (AUV) formation, information as the position and velocity of the target are naturally the final consensus status of the AUV fleet. Stability criteria are established to guarantee the consensus of the hunting system based on suitable Lyapunov–Razumikhin functional theorem, and the controller gains are derived. The cross-extended Kalman filter localization model based on bearing information is applied to improve target detection accuracy, and a velocity replacing consensus algorithm (VeRConA) is proposed to obtain the velocity of the target despite sacrificing very little of the leader’s tracking accuracy. Numerical simulation results show that the consensus theory can be applied to the target hunting task, and the proposed VeRConA algorithm is exceptionally efficient for the AUV formation to achieve sub-consensus status in the tracking process.
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