Overview of Signal Processing Techniques for Automotive Millimeter-wave Radar

被引：0
作者：
Huang Y. [1 ]
Zhang H. [1 ]
Lan L. [1 ]
Deng K. [1 ]
Yang Y. [1 ]
Zhang R. [1 ]
Liu J. [1 ]
Zhang Y. [1 ]
Wang Y. [1 ]
Zhou R. [1 ]
Xu J. [1 ]
Xi X. [1 ]
Zhang X. [1 ]
Zheng K. [1 ]
Liu Y. [1 ]
Hong W. [1 ]
机构：
[1] State Key Laboratory of Millimeter Waves, Southeast University, Nanjing
来源：
Journal of Radars | 2023年 / 12卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Automotive millimeter-wave radar; Deep learning; Interference suppression; Machine learning; Point cloud imaging; Synthetic Aperture Radar (SAR) imaging;
D O I：
10.12000/JR23119
中图分类号：
学科分类号：
摘要：
As one of the core components of Advanced Driver Assistance Systems (ADAS), automotive millimeter-wave radar has become the focus of scholars and manufacturers at home and abroad because it has the advantages of all-day and all-weather operation, miniaturization, high integration, and key sensing capabilities. The core performance indicators of the automotive millimeter-wave radar are distance, speed, angular resolution, and field of view. Accuracy, cost, real-time and detection performance, and volume are the key issues to be considered. The increasing performance requirements pose several challenges for the signal processing of millimeter-wave radar systems. Radar signal processing technology is crucial for improving radar performance to meet more stringent requirements. Obtaining dense radar point clouds, generating accurate radar imaging results, and mitigating mutual interference among multiple radar systems are the key points and the foundation for subsequent tracking, recognition, and other applications. Therefore, this paper discusses the practical application of the automotive millimeter-wave radar system based on the key technologies of signal processing, summarizes relevant research results, and mainly discusses the topics of point cloud imaging processing, synthetic aperture radar imaging processing, and interference suppression. Finally, herein, we summarize the research status at home and abroad. Moreover, future development trends for automotive millimeter-wave radar systems are forecast with the hope of enlightening readers in related fields. ©The Author(s) 2023.
引用
收藏
页码：923 / 970
页数：47
相关论文
共 144 条
[1]  
HUANG Leping, CHEN Xudong, 4D millimeter-wave radar: A new path to the popularization of smart driving, (2023)
[2]  
LI Gang, SIT Y L, MANCHALA S, Et al., Novel 4D 79 GHz radar concept for object detection and active safety applications[C], The 2019 12th German Microwave Conference(GeMiC), pp. 87-90, (2019)
[3]  
QIAN Kun, HE Zhaoyuan, ZHANG Xinyu, 3D point cloud generation with millimeter-wave radar, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4, 4, (2020)
[4]  
ENGELS F, HEIDENREICH P, WINTERMANTEL M, Et al., Automotive radar signal processing: Research directions and practical challenges[J], IEEE Journal of Selected Topics in Signal Processing, 15, 4, pp. 865-878, (2021)
[5]  
LIN Fengtai, YAN Pinpin, ZHANG Hui, Et al., Iterative closest point method for point cloud data processing of millimeter wave radar[J], Journal of Signal Processing, 39, 2, pp. 288-297, (2023)
[6]  
WEISHAUPT F, APPENRODT N, TILLY J F, Et al., PreCFAR gridmaps for automotive radar[C], The 2021 18th European Radar Conference (EuRAD), pp. 161-164, (2022)
[7]  
WEI Ziping, LI Bin, FENG Tao, Et al., Area-based CFAR target detection for automotive millimeter-wave radar[J], IEEE Transactions on Vehicular Technology, 72, 3, pp. 2891-2906, (2023)
[8]  
LAN Luhongkang, HUANG Yan, ZHENG Kaihang, Et al., Research on adaptive threshold point cloud imaging method of millimeter-wave radar[J], Journal of Signal Processing, 38, 10, pp. 2009-2020, (2022)
[9]  
DANZER A, GRIEBEL T, BACH M, Et al., 2D car detection in radar data with pointnets[C], 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 61-66, (2019)
[10]  
JIN Feng, SENGUPTA A, CAO Siyang, Et al., Mmwave radar point cloud segmentation using GMM in multimodal traffic monitoring[C], 2020 IEEE International Radar Conference(RADAR), pp. 732-737, (2020)
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