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Open AccessArticle

Exploring the Laplace Prior in Radio Tomographic Imaging with Sparse Bayesian Learning towards the Robustness to Multipath Fading

by Zhen Wang 1,*, Xuemei Guo 2,3 and Guoli Wang 2,3,*
1
School of Electronics and Information Engineering, Sun Yat-sen University, Guangzhou 510006, China
2
School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China
3
Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou 510006, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(23), 5126; https://doi.org/10.3390/s19235126
Received: 27 October 2019 / Revised: 14 November 2019 / Accepted: 15 November 2019 / Published: 22 November 2019
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
Radio tomographic imaging (RTI) is a technology for target localization by using radio frequency (RF) sensors in a wireless network. The change of the attenuation field caused by the target is represented by a shadowing image, which is then used to estimate the target’s position. The shadowing image can be reconstructed from the variation of the received signal strength (RSS) in the wireless network. However, due to the interference from multi-path fading, not all the RSS variations are reliable. If the unreliable RSS variations are used for image reconstruction, some artifacts will appear in the shadowing image, which may cause the target’s position being wrongly estimated. Due to the sparse property of the shadowing image, sparse Bayesian learning (SBL) can be employed for signal reconstruction. Aiming at enhancing the robustness to multipath fading, this paper explores the Laplace prior to characterize the shadowing image under the framework of SBL. Bayesian modeling, Bayesian inference and the fast algorithm are presented to achieve the maximum-a-posterior (MAP) solution. Finally, imaging, localization and tracking experiments from three different scenarios are conducted to validate the robustness to multipath fading. Meanwhile, the improved computational efficiency of using Laplace prior is validated in the localization-time experiment as well. View Full-Text
Keywords: radio tomographic imaging; RF sensor; received signal strength; laplace prior; multipath fading radio tomographic imaging; RF sensor; received signal strength; laplace prior; multipath fading
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Wang, Z.; Guo, X.; Wang, G. Exploring the Laplace Prior in Radio Tomographic Imaging with Sparse Bayesian Learning towards the Robustness to Multipath Fading. Sensors 2019, 19, 5126.

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