Detection Method of Radar Space Target Abnormal Motion via Local Density Peaks and Micro-Motion Feature

Micro-motion feature vectors of space targets are usually unevenly and multicluster distributed, which limits the performance of the traditional radar anomaly detection methods. To solve this problem, a novel detection method of radar space target abnormal motion method via local density peaks (LDPs) and micro-motion feature is proposed in this letter. First, two discriminative micro-motion features are extracted from the radar echoes to construct a 2-D feature space. Then the abnormal motion detector is derived by classifying the feature vectors into different clusters according to the LDPs and minimum spanning tree clustering (LDP-MST) and solving for the decision thresholds of each cluster with the LDPs, neighbors, and some preset false alarm rates. Electromagnetic simulation experiment results demonstrate that the detection rate of the proposed method is 2.49%, 5.26%, 9.63%, 15.37%, 27.99%, and 49.45% higher than six state-of-art methods, respectively, when the false alarm rate is 5%.