【摘要】 针对传统信号处理在毫米波雷达点云成像过程中点云杂点去除难和稀疏点云目标分类易出错问题，本文提出融合噪声特征的基于密度聚类的（DBSCAN）去杂点自适应聚类算法。该算法基于DBSCAN聚类算法框架，分类别构建欧式距离矩阵，快速寻找类别的中心样本点，判断并剔除异常杂点；根据中心点欧式距离和突变指数自适应调整下一帧的邻域密度和邻域半径参数；借助仿真实验验证改进算法的工程优势，进一步使用真实道路场景的实测数据验证改进算法的有效性。实验结果表明：本文所提算法不仅能去除目标点云杂点，还能自适应调整聚类参数，改善稀疏目标分类错误的问题。更多还原

【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.更多还原