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

IF 34.4 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Communications Surveys and Tutorials Pub Date : 2024-06-11 DOI:10.1109/COMST.2024.3409556

Hao Kong;Cheng Huang;Jiadi Yu;Xuemin Shen

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引用次数: 0

Abstract

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.

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基于毫米波雷达的自动驾驶汽车、智能家居和工业传感技术概览

传感技术在连接物理世界和数字世界方面起着至关重要的作用。通过将大量的物理现象转化为数字数据，它大大提高了我们对环境的理解，并在广泛的应用中发挥了重要作用。由于毫米波（mmWave）具有宽带宽和短波长的特点，雷达传感被认为是毫米波通信之外最有前途的传感技术之一。在本文中，我们提供了基于毫米波雷达的传感技术及其在自动驾驶汽车，智能家居和工业中的应用的全面调查。具体来说，我们首先从专用雷达和通信集成的角度回顾了广泛应用的毫米波雷达技术和信号处理技术，这是毫米波雷达传感的基础。然后，我们介绍了主流的机器学习技术，特别是最新的深度学习技术，用于设计毫米波信号的应用。还介绍了相关的硬件设备、可用的公共数据集和评估指标。随后，我们提供了新兴毫米波雷达传感应用的分类，并回顾了在目标检测，自我运动估计，同时定位和测绘，活动识别，姿态估计，手势识别，语音识别，生命体征监测，用户认证，室内定位，工业成像，工业测量，环境监测等方面的发展。最后讨论了本文面临的挑战和未来可能的研究方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Surveys and Tutorials COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS

CiteScore

80.20

自引率

2.50%

发文量

审稿时长

6 months

期刊介绍： IEEE Communications Surveys & Tutorials is an online journal published by the IEEE Communications Society for tutorials and surveys covering all aspects of the communications field. Telecommunications technology is progressing at a rapid pace, and the IEEE Communications Society is committed to providing researchers and other professionals the information and tools to stay abreast. IEEE Communications Surveys and Tutorials focuses on integrating and adding understanding to the existing literature on communications, putting results in context. Whether searching for in-depth information about a familiar area or an introduction into a new area, IEEE Communications Surveys & Tutorials aims to be the premier source of peer-reviewed, comprehensive tutorials and surveys, and pointers to further sources. IEEE Communications Surveys & Tutorials publishes only articles exclusively written for IEEE Communications Surveys & Tutorials and go through a rigorous review process before their publication in the quarterly issues. A tutorial article in the IEEE Communications Surveys & Tutorials should be designed to help the reader to become familiar with and learn something specific about a chosen topic. In contrast, the term survey, as applied here, is defined to mean a survey of the literature. A survey article in IEEE Communications Surveys & Tutorials should provide a comprehensive review of developments in a selected area, covering its development from its inception to its current state and beyond, and illustrating its development through liberal citations from the literature. Both tutorials and surveys should be tutorial in nature and should be written in a style comprehensible to readers outside the specialty of the article.