[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118911-en":3,"doc-seo-118911-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118911,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Recent Advances in mmWave-Radar-Based Sensing - Its Applications, and Machine Learning Techniques - A Review","Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical sensing, meteorological monitoring, industrial inspection, agriculture support, defense applications, and space systems benefit from rapid progress in mmWave radar sensor technology. A mmWave radar offers advantages over other sensors, including operation in bright, glare, or no-light environments, better antenna miniaturization, and improved range resolution. As new datasets expand, integrating radar outputs with machine learning is increasingly feasible for diverse tasks. This review summarizes performance metrics, radar bands and types, data interpretation, application domains, and machine learning methods used for mmWave-radar-based sensing.","sensors   \nReview  \nRecent Advances in mmWave-Radar-Based Sensing,  \nIts Applications, and Machine Learning Techniques: A Review  \nA. Soumya 1, C. Krishna Mohan 1 and Linga Reddy Cenkeramaddi 2, *  \nCitation: Soumya, A.; Krishna Mohan, C.; Cenkeramaddi, L.R. Recent Advances in mmWave-RadarBased Sensing, Its Applications, and Machine Learning Techniques:  \nA Review. Sensors 2023, 23, 8901 . [https://doi.org/10.3390/s23218901](https://doi.org/10.3390/s23218901)  \nAcademic Editor: Antonio Lázaro  \nReceived: 25 August 2023  \nRevised: 6 October 2023  \nAccepted: 21 October 2023  \nPublished: 1 November 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science Engineering, Indian Institute of Technology, Hyderabad 502285, India; [cs21resch15003@iith.ac.in](cs21resch15003@iith.ac.in) (A.S.); [ckm@cse.iith.ac.in](ckm@cse.iith.ac.in) (C.K.M.)  \n2 Department of Information and Communication Technology, University of Agder, 4879 Grimstad, Norway  \n* Correspondence: [linga.cenkeramaddi@uia.no](linga.cenkeramaddi@uia.no)  \nAbstract: Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar has several advantages that set it apart from other types of sensors. A mmWave radar can operate in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization than other traditional radars, and it has better range resolution. However, as more data sets have been made available, there has been a significant increase in the potential for incorporating radar data into different machine learning methods for various applications. This review focuses on key performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of the various working bands of mmWave radars, then moves on to various types of mmWave radarsand their key specifications, mmWave radar data interpretation, vast applications in various domains, and, in the end, a discussion of machine learning algorithms applied with radar data for various applications. Our review serves as a practical reference for beginners developing mmWave-radarbased applications by utilizing machine learning techniques.  \nKeywords: mmWave radar; mmWave radar applications; machine learning; industrial applications; medical applications; automotive applications; military applications; computer vision  \n1. Introduction  \nThe development of millimeter wave (mmWave) radar sensors during the past ten years has been spurred on by numerous research applications, including civilian and non-civilian applications [1,2] . With the latest improvements in chip technology and lowered cost, the mmWave radar sensor has gained widespread popularity in a wide range of applications. The mmWave radar system includes a transmitting antenna, a receiving antenna, and a signal processing system to determine an object's dynamic information, such as range, velocity, and angle of arrival (AoA) . The mmWave radar transmits a mmWave signal into space by striking an object, and this signal gets reflected. The receiving antenna captures the echo signal, which is then mixed with a transmitting signal to obtain an intermediate-frequency (IF) signal. This IF signal is processed to obtain object information. Various mmWave radars and working bands are shown in Table 1. mmWave radars operate in the frequency range between 24 GHz and 300 GHz. Pr","cbCaijBlDd7XQE8c","https://ap.wps.com/l/cbCaijBlDd7XQE8c","pdf",7631917,1,25,"English","en",105,"# Introduction\n## mmWave radar system and signal processing\n## Operational frequencies and bandwidth overview\n# Applications and performance characteristics\n## Lighting independence and resolution benefits\n## Weather penetration and multi-domain use\n# Machine learning for mmWave-radar-based sensing\n## Algorithms applied to radar data","[{\"question\":\"What key sensing tasks does mmWave radar technology support according to the review?\",\"answer\":\"The review highlights gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, and sensing in domains such as medical, meteorological, industrial, agriculture, defense, and space.\"},{\"question\":\"Why is mmWave radar advantageous compared with other sensor types?\",\"answer\":\"mmWave radar can operate in bright, glare, or no-light conditions, offers better antenna miniaturization, and provides improved range resolution, enabling more accurate range and velocity estimation.\"},{\"question\":\"How does machine learning relate to mmWave-radar-based sensing in this article?\",\"answer\":\"With the growth of available datasets, radar data can be incorporated into machine learning methods. The review focuses on key performance metrics, detailed applications, and the machine learning techniques applied to radar data for various tasks.\"}]","Recent Advances in mmWave-Radar-Based Sensing - Its Applications, and Machine Learning Techniques - A Review | PDF",1785720915,63,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"recent-advances-in-mmwave-radar-based-sensing-its-applications-and-machine-learning-techniques-a-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/recent-advances-in-mmwave-radar-based-sensing-its-applications-and-machine-learning-techniques-a-review/118911/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What key sensing tasks does mmWave radar technology support according to the review?","Question",{"text":75,"@type":76},"The review highlights gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, and sensing in domains such as medical, meteorological, industrial, agriculture, defense, and space.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is mmWave radar advantageous compared with other sensor types?",{"text":80,"@type":76},"mmWave radar can operate in bright, glare, or no-light conditions, offers better antenna miniaturization, and provides improved range resolution, enabling more accurate range and velocity estimation.",{"name":82,"@type":73,"acceptedAnswer":83},"How does machine learning relate to mmWave-radar-based sensing in this article?",{"text":84,"@type":76},"With the growth of available datasets, radar data can be incorporated into machine learning methods. 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