Clutter Filtering Algorithm in Dense Clutter Environment MultiTarget 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 TrackOriented 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.

Interactive Multimodel Tracking of a Highly Maneuvering Target using MSPDAF with Least Squares Virtual Fusion For target cognition and data fusion, the sensors are usually considered as synchronous sampling. This assumption is often no longer valid as the data rate of infrared sensors is significantly higher than that of radar. In this paper, a novel algorithm named LS IMMMSPDAF is proposed for the least squares (LS) virtual fusion method by involving time calibration due to multisensors asynchronous sampling. As for the interactive multimodel (IMM) part, it is designed for fusing multimodal observations in order to track a highly maneuvering target with different dimensions. Experimental results of two case studies have demonstrated that our algorithm can conquer the defect of asynchronous sampling, and achieve improvement in the accuracy of track estimation than MSPDAF.