基于Wi-Fi的材料传感可行性研究

Diana Zhang, Jingxian Wang, Junsu Jang, Junbo Zhang, Swarun Kumar
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引用次数: 39

摘要

无线传感技术已经展示了利用Wi-Fi信号追踪人和物体的潜力,甚至可以追踪墙后的物体。然而,在这个空间中,先前的工作旨在仅仅检测角落周围物体的存在,而不是它们的类型。在本文中,我们探讨了以下研究问题的可行性:“商品Wi-Fi无线电可以检测到周围移动物体的位置和类型吗?”我们提出了一种互补传感系统IntuWition,它可以感知环境中物体的位置和材料类型,包括那些不在视线范围内的物体。它通过使用普通Wi-Fi无线电来感知周围物体反射的无线信号,这些无线信号可以穿透墙壁和遮挡物。IntuWition的核心思想是,不同的材料以不同的方式反射和散射极化波。尽管传感设备的移动性和商用Wi-Fi无线电的硬件非理想性,但我们基于RADAR偏振法的想法来检测跨空间位置的物体的材料。一项详细的可行性研究表明,在对铜、铝、胶合板、桦木和人类这五种材料进行分类时,在视线范围内的平均准确率为95%,在非视线范围内的平均准确率为92%。最后,我们展示了我们的系统在自主无人机上的概念验证应用,该无人机使用其机载Wi-Fi无线电来感知遮挡是否是人与另一架无人机。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Feasibility of Wi-Fi Based Material Sensing
Wireless sensing has demonstrated the potential of using Wi-Fi signals to track people and objects, even behind walls.Yet, prior work in this space aims to merely detect the presence of objects around corners, rather than their type. In this paper, we explore the feasibility of the following re-search question: ?Can commodity Wi-Fi radios detect both the location and type of moving objects around them?". We present IntuWition, a complementary sensing system that can sense the location and type of material of objects in the environment, including those out of line-of-sight. It achieves this by sensing wireless signals reflected off surrounding objects using commodity Wi-Fi radios, whose signals penetrate walls and occlusions. At the core of IntuWition is the idea that different materials reflect and scatter polarized waves in different ways. We build upon ideas from RADAR Polarimetry to detect the material of objects across spatial locations, despite mobility of the sensing device and the hardware non-idealities of commodity Wi-Fi radios. A detailed feasibility study reveals an average accuracy of 95% in line-of-sight and 92% in non-line-of-sight in classifying five types of materials:copper, aluminum, plywood, birch, and human. Finally, we present a proof-of-concept application of our system on an autonomous UAV that uses its onboard Wi-Fi radios to sense whether an occlusion is a person versus another UAV.
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