Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach [PDF]
Sensors, 2018 Sensing the wideband spectrum is an important process for next-generation wireless communication systems. Spectrum sensing primarily aims at detecting unused spectrum holes over wide frequency bands so that secondary users can use them to meet their ...
Youness Arjoune, Naima Kaabouch
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Resolution Enhancement for Millimeter-Wave Radar ROI Image with Bayesian Compressive Sensing [PDF]
Sensors, 2022 For millimeter-wave (MMW) imaging security systems, the image resolution promisingly determines the performance of suspicious target detection and recognition. Conventional synthetic aperture radar (SAR) imaging algorithms only provide limited resolution
Pengfei Xie +4 more
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Sparse Reconstruction of Sound Field Using Bayesian Compressive Sensing and Equivalent Source Method [PDF]
Sensors, 2023 To solve the problem of sound field reconstruction with fewer measurement points, a sound field reconstruction method based on Bayesian compressive sensing is proposed.
Yue Xiao +4 more
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Video Compressive Sensing Reconstruction Algorithm Based on 3D Tree Structure and Bayesian Model [PDF]
Jisuanji gongcheng, 2016 In view of the traditional underwater video coding requiring higher underwater acoustic channel and the scenes of underwater video with uneven illumination being complex and not fixed,this paper presents a reconstruction algorithm with Three Dimension(3D)
ZHUANG Yanbin,WANG Zunzhi,XIAO Xianjian,ZHANG Xuewu
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Bayesian Compressive Sensing Wideband Spectrum Detection Based on Energy Efficiency [PDF]
Jisuanji gongcheng, 2016 In Cognitive Radio Network(CRN),wideband spectrum detection based on Compressive Sensing(CS) only focuses on spectral efficiency.Energy efficiency is hardly considered in spectrum detection phase,which results in larger energy consumption with the ...
WANG Zan,XU Xiaorong,YAO Yingbiao
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Variational Bayesian Compressive Sensing with Equivalent Source Modeling for Sound Field Reconstruction [PDF]
SensorsWhile conventional Bayesian compressive sensing exploits signal sparsity for accurate sound field reconstruction from under-sampled measurements, its practicality is limited by high computational complexity and slow convergence.
Yue Xiao +3 more
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Bayesian Compressive Sensing of Sparse Signals with Unknown Clustering Patterns [PDF]
Entropy, 2019 We consider the sparse recovery problem of signals with an unknown clustering pattern in the context of multiple measurement vectors (MMVs) using the compressive sensing (CS) technique. For many MMVs in practice, the solution matrix exhibits some sort of
Mohammad Shekaramiz +2 more
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Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks [PDF]
Sensors, 2020 Underwater acoustic sensor networks play an important role in assisting humans to explore information under the sea. In this work, we consider the combination of sensor selection and data routing in three dimensional underwater wireless sensor networks ...
Xuechen Chen, Wenjun Xiong, Sheng Chu
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Multi-Frequency GPR Microwave Imaging of Sparse Targets through a Multi-Task Bayesian Compressive Sensing Approach [PDF]
Journal of Imaging, 2021 An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-sparse scatterers buried within a lossy half-space. On the one hand, such an approach leverages on the wide-band nature of ground penetrating radar (GPR) data ...
Marco Salucci, Nicola Anselmi
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Compressive Sensing via Variational Bayesian Inference under Two Widely Used Priors: Modeling, Comparison and Discussion [PDF]
Entropy, 2023 Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying ...
Mohammad Shekaramiz, Todd K. Moon
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