Human sensing is significantly improving our lifestyle in many fields such as elderly healthcare and public safety. Research has demonstrated that human activity can alter the passive radio frequency (PRF) spectrum, which represents the passive reception of RF signals in the surrounding environment without actively transmitting a target signal. This paper proposes a novel passive human sensing method that utilizes PRF spectrum alteration as a biometrics modality for human authentication, localization, and activity recognition. The proposed method uses software-defined radio (SDR) technology to acquire the PRF in the frequency band sensitive to human signature. Additionally, the PRF spectrum signatures are classified and regressed by five machine learning (ML) algorithms based on different human sensing tasks. The proposed Sensing Humans among Passive Radio Frequency (SHAPR) method was tested in several environments and scenarios, including a laboratory, a living room, a classroom, and a vehicle, to verify its extensiveness. The experimental results show that the SHAPR method achieved more than 95% accuracy in the four scenarios for the three human sensing tasks, with a localization error of less than 0.8 m. These results indicate that the SHAPR technique can be considered a new human signature modality with high accuracy, robustness, and general applicability.

翻译：人体传感正在显著改善我们在老年保健和公共安全等多个领域的生活方式。研究表明，人类活动会改变被动射频（PRF）频谱，该频谱代表在不主动发射目标信号的情况下对周围环境中射频信号的被动接收。本文提出一种新颖的被动人体传感方法，利用PRF频谱变化作为生物特征模式进行人体认证、定位和活动识别。所提方法采用软件定义无线电（SDR）技术，获取对人员特征敏感的频段内的PRF。此外，基于不同的人体传感任务，通过五种机器学习（ML）算法对PRF频谱特征进行分类和回归。所提出的被动射频下人体传感（SHAPR）方法在多种环境和场景中进行了测试，包括实验室、客厅、教室和车辆，以验证其广泛适用性。实验结果表明，SHAPR方法在四种场景下针对三种人体传感任务的准确率均超过95%，定位误差小于0.8米。这些结果表明，SHAPR技术可被视为一种高精度、强鲁棒性和普遍适用性的新型人员特征模式。