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

被引:0
|
作者
Wang, Dehua [1 ]
Li, Gang [1 ]
Zhao, Zhichun [2 ,3 ]
Wang, Jianwen [1 ]
Ding, Shuai [4 ]
Wang, Kunpeng [5 ]
Duan, Meiya [5 ]
机构
[1] Tsinghua Univ, Dept Elect, Beijing 100084, Peoples R China
[2] Guangdong Lab Machine Percept & Intelligent Comp, Shenzhen 518172, Peoples R China
[3] Shenzhen MSU BIT Univ, Dept Engn, Shenzhen 518172, Peoples R China
[4] China Elect Technol Grp Corp, Res Inst 38, Hefei 230088, Peoples R China
[5] Beijing Inst Tracking & Telecommun Technol, Beijing 100094, Peoples R China
基金
中国国家自然科学基金;
关键词
Abnormal motion detection; local density peaks (LDPs); radar space target detection;
D O I
10.1109/LGRS.2023.3276421
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
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%.
引用
收藏
页数:5
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