计算机科学
聚类分析
点云
雷达
云计算
极高频率
毫米
遥感
点(几何)
人工智能
物理
地质学
光学
数学
电信
几何学
操作系统
作者
Zhicheng Bi,Yu Gao,Chaofeng Wang,Zhenghai Liu,Yaping Wan,Xiaohua Yang
标识
DOI:10.1088/1361-6501/acca3a
摘要
Abstract Millimeter-wave (mmWave) radar plays a vital role in a wide range of applications such as security surveillance and environmental monitoring. This work investigates target detection with radar point cloud measurements in the slow-motion scenario. In contrast to the existing spatial domain clustering-based target detection methods, we adopt a recursive spatial-temporal clustering (STC)-based method to detect targets in the spatial and temporal domain jointly. Specifically, the points belonging to targets are obtained by clustering with a distance metric defined in the spatial-temporal domains. In addition, to ensure the feasibility of the proposed method for practical real-time implementation, a speed-up scheme that intends to reduce the computational complexity induced by clustering in both spatial and temporal dimensions is developed. We demonstrate the efficacy of the proposed recursive STC-based method through experimental mmWave radar point cloud data where multiple people walk simultaneously in an open space. The proposed method achieves decent target detection performance improvement compared to a widely-used clustering method for target detection while its computation time is negligible compared to radar data reception time.
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