Results 21 to 30 of about 70,734 (340)
Full-Vectorial 3D Microwave Imaging of Sparse Scatterers through a Multi-Task Bayesian Compressive Sensing Approach [PDF]
Journal of Imaging, 2019 In this paper, the full-vectorial three-dimensional (3D) microwave imaging (MI) of sparse scatterers is dealt with. Towards this end, the inverse scattering (IS) problem is formulated within the contrast source inversion (CSI) framework and it is aimed ...
Marco Salucci +2 more
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Adaptive Localization in Wireless Sensor Network through Bayesian Compressive Sensing
International Journal of Distributed Sensor Networks, 2015 The estimation of the localization of targets in wireless sensor network is addressed within the Bayesian compressive sensing (BCS) framework. BCS can estimate not only target locations but also noise variance of the environment.
Zuoxin Xiahou, Xiaotong Zhang
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Structured Bayesian Compressive Sensing Exploiting Dirichlet Process Priors
Signal Processing, 2022 Qisong Wu +3 more
semanticscholar +3 more sources
Bayesian compressive sensing for cluster structured sparse signals [PDF]
Signal Processing, 2012 In traditional framework of compressive sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Other than sparse prior, structures on the sparse pattern of the signal have also been used as an additional prior, called model-based compressive sensing, such as clustered ...
Yu, Lei +3 more
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Sparse Bayesian Generative Modeling for Compressive Sensing [PDF]
Advances in Neural Information Processing Systems 37This work addresses the fundamental linear inverse problem in compressive sensing (CS) by introducing a new type of regularizing generative prior. Our proposed method utilizes ideas from classical dictionary-based CS and, in particular, sparse Bayesian learning (SBL), to integrate a strong regularization towards sparse solutions.
Benedikt Böck +2 more
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Bayesian Inference and Compressed Sensing
, 2017 This chapter provides the use of Bayesian inference in compressive sensing (CS), a method in signal processing. Among the recovery methods used in CS literature, the convex relaxation methods are reformulated again using the Bayesian framework and this method is applied in different CS applications such as magnetic resonance imaging (MRI), remote ...
Solomon A. Tesfamicael, Faraz Barzideh
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Electromagnetic ScienceThis paper presents an integrated approach for diagnosing and correcting faults in antenna arrays using a Bayesian compressive sensing (BCS) method. The proposed diagnostic technique effectively identifies both ON-OFF and partial faults with limited ...
Zi An Wang, Ping Li
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An Off-Grid Compressive Sensing Algorithm Based on Sparse Bayesian Learning for RFPA Radar [PDF]
Remote SensingIn the application of Compressive Sensing (CS) theory for sidelobe suppression in Random Frequency and Pulse Repetition Interval Agile (RFPA) radar, the off−grid issues affect the performance of target parameter estimation in RFPA radar.
Ju Wang +4 more
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Multipath Time-Delay Estimation With Impulsive Noise via Bayesian Compressive Sensing [PDF]
IEEE Signal Processing Letters, 2023 Multipath time-delay estimation is commonly encountered in radar and sonar signal processing. In some real-life environments, impulse noise is ubiquitous and significantly degrades estimation performance.
Xingyu Ji, Lei Cheng, Hangfang Zhao
semanticscholar +1 more source
Bayesian online compressed sensing [PDF]
Physical Review E, 2016 In this paper, we explore the possibilities and limitations of recovering sparse signals in an online fashion. Employing a mean field approximation to the Bayes recursion formula yields an online signal recovery algorithm that can be performed with a computational cost that is linearly proportional to the signal length per update.
Paulo V. Rossi +2 more
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