Abstract
Radio tomographic imaging (RTI) has emerged as a promising device-free localization technology for locating the targets with no devices attached. RTI deduces the location information from the reconstructed attenuation image characterizing target-induced spatial loss of radio frequency measurements in the sensing area. In cluttered indoor environments, RF measurements of wireless links are corrupted by multipath effects and thus less robust to achieve a high localization accuracy for RTI. This paper proposes to improve the quality of measurements by using spatial diversity. The key insight is that, with multiple antennae equipped, due to small-scale multipath fading, RF measurement variation of each antenna pair behaves differently. Therefore, spatial diversity can provide more reliable and strong measurements in terms of link quality. Moreover, to estimate the location from the image more precisely and make the image more identifiable, we propose using a new reconstruction regularization linearly combining the sparsity and correlation inherent in the image. The proposed reconstruction method can remarkably reduce the image noise and enhance the imaging accuracy especially in the case of a few available measurements. Indoor experimental results demonstrate that compared to existing RTI improvement methods, our RTI solution can reduce the root-mean-square localization error at least 47% while also improving the imaging performance.
Highlights
In many scenarios, for example, searching for survivors in the disaster area, rescuing hostages and finding the criminals [1], it is critical to locate the target carrying no devices
received signal strength (RSS) map establishment, we focus our interest on the model-based device-free localization (DFL) method, radio tomographic imaging (RTI)
We have presented some improvements to the localization and imaging performance of RTI in challenging environments
Summary
For example, searching for survivors in the disaster area, rescuing hostages and finding the criminals [1], it is critical to locate the target carrying no devices. As an effective radio frequency (RF)-based DFL, radio tomographic imaging (RTI) [2] using received signal strength (RSS) has attracted considerable attention in the past decade since RSS measurements are readily available in most wireless commercial off-the-shelf (COTS) devices. RTI can be implemented on the existing network without any extra hardware, which offers a cost-effective DFL solution. Since radio signals can penetrate walls and other non-metallic structures, RTI is able to find the target hiding behind obstacles. RTI has been successfully applied to through-wall target tracking [3,4], residential monitoring [5,6], roadside surveillance [7], obstacle mapping [8,9] and health care [10,11]
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