The Effect of Primary User Bandwidth on Bayesian Compressive Sensing Based Spectrum Sensing [PDF]
, 2016 The application of compressive sensing (CS) theory has found great interest in wideband spectrum sensing. Although most studies have considered perfect reconstruction of the primary user signal it is actually more important to assess the presence or ...
Cirpan, Hakan Ali +2 more
core +1 more source
Radio Tomographic Imaging Based on Low-Rank and Sparse Decomposition
IEEE Access, 2019 Imaging artifacts induced by the multipath interference in Radio-Frequency sensing network usually significantly degrade the performance of Radio Tomographic Imaging (RTI) and thereby has become a major challenge in the Device-Free Localization (DFL ...
Jiaju Tan +4 more
doaj +1 more source
Updating Wireless Signal Map with Bayesian Compressive Sensing [PDF]
, 2016 In a wireless system, a signal map shows the signal strength at different locations termed reference points (RPs). As access points (APs) and their transmission power may change over time, keeping an updated signal map is important for applications such ...
Chan, S. H.Gary +5 more
core +1 more source
Bayesian method for image recovery from block compressive sensing [PDF]
, 2016 We consider the problem of recovering an image using block compressed sensing (BCS). Traditional BCS algorithms recovers each image block independently and utilizes post-processing methods for removing the blocking artifacts.
Latva-aho, M. +5 more
core +3 more sources
Bayesian Compressive Sensing for DOA Estimation using the Difference Coarray [PDF]
, 2020 -In this paper, we utilize Bayesian Compressive Sensing (BCS) for direction-of-arrival (DOA) estimation based on the coarray. This enables estimation of more sources than the number of physical antennas. We adopt the covariance vectorization technique to
Xiangrong Wang +3 more
core
Development of an Efficient Response Surface Method for Highly Nonlinear Systems from Sparse Sampling Data Using Bayesian Compressive Sensing [PDF]
, 2021 A main challenge for risk assessment on geotechnical systems is the computational effort required when stochastic sampling methods are used. Because the deterministic models used for geotechnical systems are often complicated and highly nonlinear, it is ...
Li, Peiping, Wang, Yu
core +1 more source
An Enhanced Bayesian Compressive Sensing Method of Moments for Monostatic Scattering Problems
Applied Computational Electromagnetics Society Journal (ACES)In this paper, an Enhanced Bayesian Compressive Sensing method based on the Method of Moments (EBCS-MoM) is proposed to accelerate the solution of three-dimensional electromagnetic scattering problems.
Longhui Sun, Zhonggen Wang, Chenlu Li
semanticscholar +1 more source
Sensor Deployment in Bayesian Compressive Sensing Based Environmental Monitoring [PDF]
, 2014 Sensor networks play crucial roles in the environmental monitoring. So far, the large amount of resource consumption in traditional sensor networks has been a huge challenge for environmental monitoring.
Yan, Shulin +7 more
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Frequency-hopping GPR prospecting of sparse scatterers through Bayesian compressive sensing [PDF]
, 2017 International audienceAn innovative ground penetrating radar (GPR) inverse scattering (IS) methodology for imaging sparse buried objects is proposed. The developed methodology integrates a customized Bayesian compressive sensing (BCS) solver within a ...
Tenuti, Lorenza +7 more
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Contrast source inversion of sparse targets through multi-resolution Bayesian compressive sensing
Inverse ProblemsThe retrieval of non-Born scatterers is addressed within the contrast source inversion (CSI) framework by means of a novel multi-step inverse scattering method that jointly exploits prior information on the class of targets under investigation and ...
M. Salucci +5 more
semanticscholar +1 more source