Density Cluster-Based Clutter Removal Technology for Millimeter-Wave Radar Target Point Cloud
LIU Qi1，2， HE Yifei3， GU Ming1，2， CHEN Zihao1，2， LI Yunhao4，5， WANG Tao2
1. AVIC Leihua Electronic Technology Research Institute， Wuxi 214063， Jiangsu， China；
2. College of Electronics and Information， Northwestern Polytechnical University， Xi'an 710072， Shaanxi， China；
3. Aviation Military Representative Office for the Army Equipment Department Stationed in Shanghai， Shanghai 200031， China； 4. Southwest China Research Institute of Electronic Equipment，Chengdu 610036， Sichuan， China；
5. National Key Laboratory of Electromagnetic Space Security，Chengdu 610036，Sichuan， China
Abstract：This paper introduces an improved adaptive DBSCAN clustering algorithm to tackle the issues of point cloud clutter removal and sparse target classification in radar imaging systems, which are traditionally handled by signal processing methods. The proposed method constructed a Euclidean distance matrix for classification, rapidly identified central sampleswithin categories, detected and eliminated anomalous stray points, and adaptively adjusted the neighbourhood density and radius parameters for future frames based on the Euclidean distances and mutation indices of the central points. Initially, the engineering advantages of the improved algorithm were validated through simulation experiments, followed by further verification using real-world road scene data to confirm its practical effectiveness. Experimental results show that the proposed algorithm effectively eliminates clutter from target point clouds and dynamically adjusts clustering parameters to reduce sparse classification errors in targets.
刘琦, 贺轶斐, 顾铭, 陈梓浩, 李昀豪, 汪涛. 基于密度聚类的毫米波雷达目标点云杂点去除技术[J]. 空天防御, 2026, 9(1): 63-72.
LIU Qi, HE Yifei, GU Ming, CHEN Zihao, LI Yunhao, WANG Tao. Density Cluster-Based Clutter Removal Technology for Millimeter-Wave Radar Target Point Cloud. Air & Space Defense, 2026, 9(1): 63-72.
