Radio Tomographic Imaging with Feedback-Based Sparse Bayesian Learning

2018 ◽  
Author(s):  
Zhen Wang ◽  
Hang Su ◽  
Xuemei Guo ◽  
Guoli Wang
Keyword(s):  
Bayesian Learning ◽  
Tomographic Imaging ◽  
Sparse Bayesian Learning ◽  
Radio Tomographic Imaging

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

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5126 ◽  
Author(s):  
Wang ◽  
Guo ◽  
Wang
Keyword(s):  
Wireless Network ◽  
Bayesian Learning ◽  
Signal Reconstruction ◽  
Multipath Fading ◽  
Tomographic Imaging ◽  
Sparse Bayesian Learning ◽  
Attenuation Field ◽  
Radio Tomographic Imaging ◽  
Map Solution ◽  
Localization And Tracking

Radio tomographic imaging (RTI) is a technology for target localization by using radiofrequency (RF) sensors in a wireless network. The change of the attenuation field caused by thetarget 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 RSSvariations are reliable. If the unreliable RSS variations are used for image reconstruction, someartifacts will appear in the shadowing image, which may cause the target’s position being wronglyestimated. Due to the sparse property of the shadowing image, sparse Bayesian learning (SBL) canbe 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 frameworkof SBL. Bayesian modeling, Bayesian inference and the fast algorithm are presented to achieve themaximum-a-posterior (MAP) solution. Finally, imaging, localization and tracking experiments fromthree 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-timeexperiment as well.

Blind Identification of Multichannel Systems Based on Sparse Bayesian Learning

2016 ◽  
Vol E99.B (12) ◽  
pp. 2614-2622 ◽  
Author(s):  
Kai ZHANG ◽  
Hongyi YU ◽  
Yunpeng HU ◽  
Zhixiang SHEN ◽  
Siyu TAO
Keyword(s):  
Bayesian Learning ◽  
Blind Identification ◽  
Sparse Bayesian Learning ◽  
Multichannel Systems

scholarly journals Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework

NeuroImage ◽  
2021 ◽  
pp. 118309
Author(s):  
Ali Hashemi ◽  
Chang Cai ◽  
Gitta Kutyniok ◽  
Klaus-Robert Müller ◽  
Srikantan S. Nagarajan ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Bayesian Learning ◽  
Learning Algorithms ◽  
Sparse Bayesian Learning

Speech Signal Recovery Using Block Sparse Bayesian Learning

2019 ◽  
Vol 45 (3) ◽  
pp. 1567-1579
Author(s):  
Irfan Ahmed ◽  
Aftab Khan ◽  
Nasir Ahmad ◽  
NasruMinallah ◽  
Hazrat Ali
Keyword(s):  
Speech Signal ◽  
Bayesian Learning ◽  
Signal Recovery ◽  
Sparse Bayesian Learning
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