{"title":"使用 COTS 毫米波雷达探测和识别不合作无人机","authors":"Yuan He, Jia Zhang, Rui Xi, Xin Na, Yimian Sun, Beibei Li","doi":"10.1145/3638767","DOIUrl":null,"url":null,"abstract":"<p>Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This paper presents mmHawkeye, a passive approach for non-cooperative UAV detection and identification with a COTS millimeter wave (mmWave) radar. mmHawkeye doesn’t require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV’s periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low-SNR and uncertain reflected signals from the UAV. After analyzing the theoretical model of the PMM feature, mmHawkeye can further track the UAV’s position containing range, azimuth and altitude angle with dynamic programming and particle filtering, and then identify it with a Long Short-Term Memory (LSTM) based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80m.</p>","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":"119 1","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection and Identification of non-cooperative UAV using a COTS mmWave Radar\",\"authors\":\"Yuan He, Jia Zhang, Rui Xi, Xin Na, Yimian Sun, Beibei Li\",\"doi\":\"10.1145/3638767\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This paper presents mmHawkeye, a passive approach for non-cooperative UAV detection and identification with a COTS millimeter wave (mmWave) radar. mmHawkeye doesn’t require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV’s periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low-SNR and uncertain reflected signals from the UAV. After analyzing the theoretical model of the PMM feature, mmHawkeye can further track the UAV’s position containing range, azimuth and altitude angle with dynamic programming and particle filtering, and then identify it with a Long Short-Term Memory (LSTM) based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80m.</p>\",\"PeriodicalId\":50910,\"journal\":{\"name\":\"ACM Transactions on Sensor Networks\",\"volume\":\"119 1\",\"pages\":\"\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2023-12-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Sensor Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3638767\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Sensor Networks","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3638767","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Detection and Identification of non-cooperative UAV using a COTS mmWave Radar
Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This paper presents mmHawkeye, a passive approach for non-cooperative UAV detection and identification with a COTS millimeter wave (mmWave) radar. mmHawkeye doesn’t require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV’s periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low-SNR and uncertain reflected signals from the UAV. After analyzing the theoretical model of the PMM feature, mmHawkeye can further track the UAV’s position containing range, azimuth and altitude angle with dynamic programming and particle filtering, and then identify it with a Long Short-Term Memory (LSTM) based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80m.
期刊介绍:
ACM Transactions on Sensor Networks (TOSN) is a central publication by the ACM in the interdisciplinary area of sensor networks spanning a broad discipline from signal processing, networking and protocols, embedded systems, information management, to distributed algorithms. It covers research contributions that introduce new concepts, techniques, analyses, or architectures, as well as applied contributions that report on development of new tools and systems or experiences and experiments with high-impact, innovative applications. The Transactions places special attention on contributions to systemic approaches to sensor networks as well as fundamental contributions.