摘要
针对毫米波调频连续波多输入多输出(frequency modulated continuous wave multiple-input multiple-output,FMCW MIMO)雷达点云目标远疏近密的密度不一问题,提出了一种基于密度聚类的稳健自适应三维点云聚类方法。首先,从原始数据中提取目标的距离、方位角以及俯仰角信息;其次,结合雷达的距离分辨率和角度分辨率将提取得到的三维信息以体素形式进行表示,并计算每个体素相应的局部度量值;再次,根据局部度量值计算各个体素的聚类搜索区域;最后,结合遗传算法(genetic algorithm,GA)自适应寻找聚类过程中所需的最佳参数并实现聚类。实验结果表明,该方法能够实现毫米波雷达三维点云的有效聚类。
Aiming at the different density of point cloud targets in millimeter wave frequency modulated continuous wave multiple input multiple output(FMCW MIMO)radar,a robust adaptive 3D point cloud clustering method based on density clustering was proposed.Firstly,the distance,azimuth and pitch information of the target were extracted from the original data.Secondly,combined with the range resolution and angle resolution of radar,the extracted three-dimensional information was expressed in the form of voxels,and the corresponding local measurements of each voxel were calculated.Thirdly,the clustering search area of each voxel was calculated according to the local measurement.Finally,combined with genetic algorithm(GA),the best parameters needed in the clustering process was adaptively found,and clustering was realized.The results show that the method can cluster 3D point cloud obtained from millimeter wave radar effectively.
作者
钟晋孝
晋良念
ZHONG Jin-xiao;JIN Liang-nian(Information and Communication College, Guilin University of Electronic Technology, Guilin 541004, China;Guangxi Key Lab of Wireless Wideband Communication & Signal Processing, Guilin 541004, China)
出处
《科学技术与工程》
北大核心
2022年第5期1936-1943,共8页
Science Technology and Engineering
基金
国家自然科学基金(61461012)
广西自然科学基金(2017GXNSFAA198050)
广西无线宽带通信与信号处理重点实验室2020年主任基金项目
桂林电子科技大学研究生教育创新计划(2020YCXS023)。
关键词
毫米波雷达
点云聚类
稳健自适应
遗传算法
millimeter wave radar
point cloud clustering
robust and adaptive
genetic algorithm
