Target Tracking with Underwater Sensor Networks Based on Grubbs Criterion and Improved Particle Filter Algorithm

Abstract

When the Underwater Wireless Sensor Network (UWSN) performs target tracking, the contributions of the measured values of the nodes are different, and the battery energy carried by the sensor node is limited. Therefore, a good node fusion weight method and node planning mechanism can obtain better tracking performance. A distributed particle filter target tracking algorithm based on Grubbs criterion and Mutual Information Entropy Weighted (GMIEW) fusion is proposed to solve the above problem in this paper. Firstly, the Grubbs criterion is used to analyze and verify the information obtained by the sensor nodes before the information fusion, and the interference information and error information are removed. Secondly, in the process of calculating the importance weight of particle filter, the dynamic weighting factor is introduced. The mutual information entropy between the measured value of the sensor node and the target state is used to reflect the amount of target information provided by the sensor node, so as to obtain the corresponding weighting factor of each node. Finally, the improved cluster-tree network topology is used to track the target in three-dimensional space. Simulation results show that the proposed algorithm improves greatly the accuracy of underwater sensor measurement data for target tracking prediction and reduces the tracking error.