RTI Goes Wild: Radio Tomographic Imaging for Outdoor People Detection and Localization
RTI Goes Wild: Radio Tomographic Imaging for Outdoor People Detection and Localization
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DOI：
10.1109/tmc.2015.2504965
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发表时间：
2014-07
影响因子：
7.9
通讯作者：
C. Alippi;M. Bocca;G. Boracchi;Neal Patwari;M. Roveri
中科院分区：
文献类型：
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作者：
C. Alippi;M. Bocca;G. Boracchi;Neal Patwari;M. Roveri
近年来，射频（RF）传感器网络已被用于定位室内的人，而无需他们佩戴侵入性电子设备。这些由低功率无线电收发器形成的无线网状网络持续测量链路的接收信号强度（RSS）。无线电断层成像（RTI）是一种从这些RSS测量开始生成无线电收发器所覆盖的区域内的电磁场变化的2D图像的技术，以发现动物（例如，人，大型动物）或大型金属物体（例如，汽车）。在这里，我们提出了一个RTI系统，用于定位和跟踪户外的人。与室内环境相比，室外RSS信号是时变的，例如，由于降雨或风驱动的树叶。我们提出了一种新的户外RTI方法，尽管非平稳噪声引入的RSS数据的环境，实现了高定位精度，并大大降低了传感单元的能耗。实验结果表明，该系统准确地检测和跟踪一个人在不同的环境条件下，在一个大的森林区域实时，显着减少误报，定位误差和能源消耗相比，最先进的RTI方法。
In recent years, Radio frequency (RF) sensor networks have been used to localize people indoor without requiring them to wear invasive electronic devices. These wireless mesh networks, formed by low-power radio transceivers, continuously measure the received signal strength (RSS) of the links. Radio Tomographic Imaging (RTI) is a technique that generates, starting from these RSS measurements, 2D images of the change in the electromagnetic field inside the area covered by the radio transceivers to spot the presence and movements of animates (e.g., people, large animals) or large metallic objects (e.g., cars). Here, we present a RTI system for localizing and tracking people outdoors. Differently than in indoor environments where the RSS does not change significantly with time unless people are found in the monitored area, the outdoor RSS signal is time-variant, e.g., due to rainfall or wind-driven foliage. We present a novel outdoor RTI method that, despite the nonstationary noise introduced in the RSS data by the environment, achieves high localization accuracy and dramatically reduces the energy consumption of the sensing units. Experimental results demonstrate that the system accurately detects and tracks a person in real-time in a large forested area under varying environmental conditions, significantly reducing false positives, localization error and energy consumption compared to state-of-the-art RTI methods.