使用 COTS 毫米波雷达探测和识别不合作无人机

IF 3.9 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Sensor Networks Pub Date : 2023-12-27 DOI:10.1145/3638767
Yuan He, Jia Zhang, Rui Xi, Xin Na, Yimian Sun, Beibei Li
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引用次数: 0

摘要

小型无人飞行器(UAV)正成为安全敏感区域和个人隐私的潜在威胁。无人飞行器可以在高空拍摄照片,但如何探测这种不请自来的入侵者却是一个未决问题。本文介绍了一种利用 COTS 毫米波(mmWave)雷达进行非合作式无人机探测和识别的被动方法--毫米鹰眼(mmHawkeye)。毫米鹰眼无需事先了解无人机的类型、运动和飞行轨迹,同时利用无人机周期性微动(PMM)引起的信号特征进行远距离精确探测。因此,该设计能有效处理来自无人机的低 SNR 和不确定反射信号。在分析了 PMM 特征的理论模型后,mmHawkeye 可以通过动态编程和粒子滤波进一步跟踪无人机的位置(包括距离、方位角和高度角),然后使用基于长短期记忆(LSTM)的检测器对其进行识别。我们在商用毫米波雷达上实现了 mmHawkeye,并评估了其在不同设置下的性能。实验结果表明,mmHawkeye 的探测精度高达 95.8%,探测距离可达 80 米。
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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.

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来源期刊
ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks 工程技术-电信学
CiteScore
5.90
自引率
7.30%
发文量
131
审稿时长
6 months
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.
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