Feature-Aided Passive Tracking of Noncooperative Multiple Targets Based on the Underwater Sensor Networks
Yiwei Tian, Meiqin Liu, Senlin Zhang, Ronghao Zheng, Zhen Fan
Abstract
Passive detection can work for a long time with low energy consumption in underwater surveillance. However, tracking unknown noncooperative targets with only direction angles is challenging, and the tracking performance of multiple targets is poor. Based on several passive sensors in the underwater sensor network (UWSN), a feature-aided state estimation method is used to start tracking unknown targets. The feature-aided joint probabilistic data association combined with the particle filter method is also proposed to improve the passive tracking performance of multiple targets. The track management and the fusion strategy are given to remove fake tracks and obtain correct trajectories of unknown targets. The simulation results show that the feature-aided method can quickly start and effectively track multiple noncooperative targets with passive sensors. Compared with other methods, the proposed method can track targets more accurately with the advantages of low energy consumption and less exposure in various environments.