空天防御2026，Vol.9Issue(1)：63-72,10.
基于密度聚类的毫米波雷达目标点云杂点去除技术
Density Cluster-Based Clutter Removal Technology for Millimeter-Wave Radar Target Point Cloud
摘要
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.关键词
毫米波雷达/点云成像/自适应聚类/稀疏目标分类/杂点去除Key words
millimeter wave radar/point cloud imaging/adaptive cluster/sparse target classification/noise point removal分类
信息技术与安全科学引用本文复制引用
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):63-72,10.基金项目
中国博士后科学基金资助项目(2024M764267) （2024M764267）
航空科学基金资助项目(20240020053003,201920053001) （20240020053003,201920053001）
电磁空间安全全国重点实验室开放基金资助项目 （）
西北工业大学博士论文创新基金资助项目 （）
