$Radar^2$：利用商用现货（COTS）毫米波雷达实现被动间谍雷达检测与定位
$Radar^2$: Passive Spy Radar Detection and Localization using COTS mmWave Radar
AI总结：
针对毫米波雷达被恶意用于隐私监控的风险，提出$Radar^2$系统，用单台COTS毫米波雷达，通过频率分量检测、CNN波形分类和三角测量，实现多台间谍雷达的高准确率检测与米级内定位，且环境鲁棒性强。
摘要：
毫米波（mmWave）雷达凭借其非侵入性特性与高测距精度，已在人体跟踪、健康监测、自动驾驶等众多领域得到应用。然而，这些能力若被用于恶意目的，也会引发严重的安全与隐私问题。例如，用户的日常生活可能会被间谍雷达秘密监控。因此，迫切需要开发能够检测并定位此类间谍雷达的系统。本文提出了$Radar^2$，这是一款实用的系统，可通过单台商用现货（COTS）毫米波雷达实现被动间谍雷达的检测与定位。具体而言，我们提出了一种新颖的“频率分量检测”方法来检测毫米波信号的存在，利用基于卷积神经网络（CNN）的波形分类器区分毫米波雷达信号与WiGig信号，并基于检测器在多个锚点处的观测结果，通过三角测量法实现间谍雷达的定位。$Radar^2$不仅适用于不同类型的毫米波雷达，还能同时检测并定位多台雷达。最后，我们开展了大量实验，在多种场景下评估了$Radar^2$的有效性与鲁棒性。评估结果显示，其雷达检测率超过96%，定位误差在0.3米以内。结果还表明，$Radar^2$对各类环境因素（例如房间布局、人类活动）均具备鲁棒性。
英文摘要：
Millimeter-wave (mmWave) radars have found applications in a wide range of domains, including human tracking, health monitoring, and autonomous driving, for their unobtrusive nature and high range accuracy. These capabilities, however, if used for malicious purposes, could also result in serious security and privacy issues. For example, a user's daily life could be secretly monitored by a spy radar. Hence, there is a strong urge to develop systems that can detect and locate such spy radars. In this paper, we propose $Radar^2$, a practical system for passive spy radar detection and localization using a single commercial off-the-shelf (COTS) mmWave radar. Specifically, we propose a novel \textit{Frequency Component Detection} method to detect the existence of mmWave signals, distinguish between mmWave radar and WiGig signals using a waveform classifier based on a convolutional neural network (CNN), and localize spy radars using triangulation based on the detector's observations at multiple anchor points. Not only does $Radar^2$ work for different types of mmWave radar, but it can also detect and localize multiple radars simultaneously. Finally, we performed extensive experiments to evaluate the effectiveness and robustness of $Radar^2$ in various settings. Our evaluation results show that the radar detection rate is above 96$\%$ and the localization error is within 0.3m. The results also reveal that $Radar^2$ is robust against various environmental factors (e.g., room layout and human activities).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
