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Abstract: Because underwater sensor networks (USNs) have limited energy resources due to environmental constraints, it is essential to improve energy utilization. For this purpose, an autonomous underwater vehicle (AUV) with greater onboard computation power is used to process measurement data, and the mobility of the AUV is leveraged to optimize the USN topology, enhancing tracking accuracy. First, to address the transmission delay of underwater acoustic signals, a time-delay compensated centralized extended Kalman filter (TD-CEKF) algorithm is proposed. Next, the mathematical relationship between AUV position and USN topology is established, based on which the optimization target is constructed. Subsequently, a penalty function is introduced to remove the constraints from the objective function, and the optimal AUV position is searched using the gradient descent method to optimize the USN topology. The simulation results demonstrate that the proposed algorithm can effectively overcome the influence of transmission delay on target tracking and achieve improved tracking performance.

面向水下应用的水下自主航行器与传感器网络联合目标跟踪方法

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