Device-free activity recognition (DFAR) is a promising technique that detects the activity of a target by analyzing the influence of its existence on surrounding wireless links. It realizes target sensing without the participation or even awareness of the target. The key question of DFAR is how to characterize the influence of the target on wireless links. Existing works mostly utilize statistical features, such as mean and variance in time-domain, and energy as well as entropy in frequency-domain, to characterize the influenced signals. However, statistical features provide only partial information. This paper explores the method on how to characterize the distribution of the signal as a whole. Specifically, we present a novel coherence histogram, which leverages the spatial structural characteristics to better characterize the distribution of the wireless signal. The coherence histogram captures not only the occurrence probability of received signal strength (RSS) measurements, but also the spatial relationship between adjacent RSS measurements as well. Experimental results show that our coherence histogram-based DFAR system could achieve an accuracy of more than 96%, which significantly outperforms other state-of-the-art DFAR systems remarkably.

中文翻译：

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基于相干直方图的无设备活动识别


无设备活动识别（DFAR）是一种很有前途的技术，它通过分析目标的存在对周围无线链路的影响来检测目标的活动。它实现了无需目标参与甚至意识的目标感知。 DFAR的关键问题是如何表征目标对无线链路的影响。现有的工作大多利用统计特征，例如时域中的均值和方差，以及频域中的能量和熵来表征受影响的信号。然而，统计特征仅提供部分信息。本文探讨了如何表征信号整体分布的方法。具体来说，我们提出了一种新颖的相干直方图，它利用空间结构特征来更好地表征无线信号的分布。相干直方图不仅捕获接收信号强度 (RSS) 测量的发生概率，还捕获相邻 RSS 测​​量之间的空间关系。实验结果表明，我们基于相干直方图的 DFAR 系统可以达到 96% 以上的准确率，明显优于其他最先进的 DFAR 系统。