Underwater Target Tracking Based on the Feature-Aided GM-PHD Method

Targets in water usually have the characteristics of wideband or multifrequency. Affected by the multipath effect and the interference of random noise, a target in water will generate multiple direction observations based on the passive nodes of an underwater sensor network (UWSN), which will also produce multiple localization measurements. The Gaussian mixture (GM) probability hypothesis density (PHD) filter is an effective method for tracking targets with uncertain measurements. By analyzing the features of underwater noncooperative targets, the feature-aided method is proposed in this article to deal with the problem of tracking unknown targets. A feature-aided measurement partition (FAMP) algorithm is used to obtain underwater sufficient and effective measurements. The traditional GM PHD method is improved by feature-aiding, and the threshold of selecting components is adjusted adaptively in the tracking process. The simulation results show that our method improves the accuracy of estimating the number of underwater unknown targets by about 20% and has a better performance in tracking compared with the original filter.