An Improved Active Sonar Automatic Tracking Method using Spatial Smoothing and PHD Filtering
Pengfei Shao, Jun Wang, Xinyu Gu · 2019
For monostatic pulsed active sonar detection performance decreased under strange waters where targets and interferences were unknow. This paper proposed a pre-test automatic tracking method, on the one hand, by using spatial smoothing to extract targets' spatial continuity characteristics and reduce impact of random clutter, and on the other hand, by using probability density hypothesis (PHD) filtering to further filter out the clutter, at the same time output the estimation of underwater targets' state and number, and thus help to reduce detection false alarm rate and miss rate, improves performance of the active sonar actual use. In this paper, an improved active sonar automatic tracking framework is presented, and the method is verified by sea trial data.