Clutter Filtering Algorithm in Dense Clutter Environment

Abstract

Multi-Target Tracking (MTT) in dense clutter environment has always been a research difficulty in the field of radar target tracking, the key is to effectively combine state filtering with data association. In the dense clutter environment, in addition to the echo of the target point, there are also a large number of clutter interference from unknown scatters, so it is difficult to process the data. In this paper, we propose a clutter filtering algorithm in dense clutter environment based on Track-Oriented Multiple Hypothesis Tracking (TOMHT) and Support Vector Machine (SVM), which is used to filter clutters, and to provide prior environmental information for subsequent target tracking. It reduces the density of clutter and improves the efficiency of data association under the premise of satisfying the tracking accuracy. The results show that the algorithm can effectively suppress clutter and improve tracking performance.