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Recursive spatial-temporal clustering-based target detection with millimeter-wave radar point cloud

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成果类型:
期刊论文
作者:
Bi, Zhicheng;Gao, Yu;Wang, Chaofeng;Liu, Zhenghai;Wan, Yaping;...
通讯作者:
Chaofeng Wang
作者机构:
[Bi, Zhicheng] Univ South China, Sch Elect Engn, Hengyang 421001, Peoples R China.
[Liu, Zhenghai; Yang, Xiaohua; Gao, Yu; Wang, Chaofeng; Wan, Yaping] Univ South China, Sch Comp Sci, Hengyang 421001, Peoples R China.
通讯机构:
[Chaofeng Wang] S
School of Computer Science, University of South China, Hengyang 421001, People's Republic of China
语种:
英文
关键词:
millimeter-wave radar;radar point cloud;target detection;spatial-temporal clustering
期刊:
Measurement Science And Technology
ISSN:
0957-0233
年:
2023
卷:
34
期:
7
页码:
075110
基金类别:
National Natural Science Foundation of China [62201248]; Startup Foundation of the University of South China [200XQD056]
机构署名:
本校为第一机构
院系归属:
电气工程学院
摘要:
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. ...

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