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Open AccessArticle

Compressive Sensing Based Radio Tomographic Imaging with Spatial Diversity

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
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Sensors 2019, 19(3), 439; https://doi.org/10.3390/s19030439
Received: 7 December 2018 / Revised: 4 January 2019 / Accepted: 16 January 2019 / Published: 22 January 2019
(This article belongs to the Special Issue Data and Information Fusion for Wireless Sensor Networks)
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. View Full-Text
Keywords: radio tomographic imaging; spatial diversity; compressive sensing; RSS; indoor localization radio tomographic imaging; spatial diversity; compressive sensing; RSS; indoor localization
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Xu, S.; Liu, H.; Gao, F.; Wang, Z. Compressive Sensing Based Radio Tomographic Imaging with Spatial Diversity. Sensors 2019, 19, 439.

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