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Sensors 2019, 19(8), 1828; https://doi.org/10.3390/s19081828

Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel Diversity

College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China
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Received: 8 March 2019 / Revised: 13 April 2019 / Accepted: 15 April 2019 / Published: 17 April 2019
(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)
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Abstract

As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings are vulnerable to environmental dynamics. The deviation between runtime RSS variations and the data in a fixed dictionary can significantly deteriorate the performance of DFL. In this paper, we introduce ComDec, a novel CS-based DFL method using channel state information (CSI) to enhance localization accuracy and robustness. To exploit the channel diversity of CSI measurements, the DFL problem is formulated as a joint sparse recovery problem that recovers multiple sparse vectors with common support. To solve this problem, we develop a joint sparse recovery algorithm under the variational Bayesian inference framework. In this algorithm, dictionaries are parameterized based on the saddle surface model. To adapt to the environmental changes and different channel characteristics, dictionary parameters are modelled as tunable parameters. Simulation results verified the superior performance of ComDec as compared with other state-of-the-art CS-based DFL methods. View Full-Text
Keywords: changing environment; channel diversity; variational Bayesian inference; joint sparse recovery; device-free localization changing environment; channel diversity; variational Bayesian inference; joint sparse recovery; device-free localization
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Yu, D.; Guo, Y.; Li, N.; Yang, X. Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel Diversity. Sensors 2019, 19, 1828.

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