A Survey of mmWave Radar-Based Sensing in Autonomous Vehicles, Smart Homes and Industry

Sensing technology plays a crucial role in bridging the physical and digital worlds. By transforming a multitude of physical phenomena into digital data, it significantly enhances our understanding of the environment and is instrumental in a wide range of applications. Given the wide bandwidth and s...

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Veröffentlicht in:IEEE Communications surveys and tutorials Jg. 27; H. 1; S. 463 - 508
Hauptverfasser: Kong, Hao, Huang, Cheng, Yu, Jiadi, Shen, Xuemin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.02.2025
Schlagworte:
autonomous vehicle
Deep learning
industry
Millimeter wave communication
Millimeter wave radar
Radar
Radar detection
Radar imaging
radar signal processing
Sensors
smart home
Surveys
wireless sensing
ISSN:2373-745X
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Zusammenfassung:Sensing technology plays a crucial role in bridging the physical and digital worlds. By transforming a multitude of physical phenomena into digital data, it significantly enhances our understanding of the environment and is instrumental in a wide range of applications. Given the wide bandwidth and short wavelength characteristics, millimeter wave (mmWave) radar sensing is considered one of the most promising sensing techniques beyond mmWave communication. In this paper, we provide a comprehensive survey of mmWave radar-based sensing techniques and applications in autonomous vehicles, smart homes, and industry. Specifically, we first review widely exploited mmWave radar techniques and signal processing techniques from the perspective of dedicated radars and communication integration, which are the basis of mmWave radar sensing. Then, we introduce mainstream machine learning techniques, especially the latest deep learning techniques for designing applications with mmWave signals. Related hardware devices, available public datasets, and evaluation metrics are also presented. Afterward, we provide a taxonomy of emerging mmWave radar sensing applications, and review the developments in object detection, ego-motion estimation, simultaneous localization and mapping, activity recognition, pose estimation, gesture recognition, speech recognition, vital sign monitoring, user authentication, indoor positioning, industrial imaging, industrial measurement, environmental monitoring, etc. We conclude the paper by discussing challenges and potential future research directions.
ISSN:2373-745X
DOI:10.1109/COMST.2024.3409556