Node Depth Adjustment Based Target Tracking in Sparse Underwater Sensor Networks

Abstract

Due to the limited energy in underwater sensor networks, underwater nodes need to be deployed sparsely. However, sparse USNs will lead to poor tracking coverage and detection capability. To solve these problems, the mobility of nodes in depth can be utilized to optimize the node topology to achieve data fusion more reliably and effectively. In this paper, for underwater target tracking, a node depth adjustment algorithm is proposed. Firstly, after introducing the sound velocity profile on acoustic signal transmission, the asynchronous particle filter algorithm based on delay estimation is improved, which makes the filter more suitable for an underwater environment. Secondly, the influence of node topology on the tracking accuracy is analyzed, and the optimization problem of node depth adjustment is constructed, in which the depth-related Fisher Information Matrix is designed as the optimization criterion. Thirdly, for scenarios in which the target depth is either known or unknown, the analytical method and the interior point method are employed to solve the problem, respectively, and the optimal depth adjustment strategies in corresponding scenarios are obtained. The simulation results show that the proposed algorithm can fully adjust the node depth and achieve a more accurate tracking performance.