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
doaj +8 more sources
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
doaj +7 more sources
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
doaj +5 more sources
A Sound Source Identification Algorithm Based on Bayesian Compressive Sensing and Equivalent Source Method [PDF]
Sensors, 2020 Near-field acoustic holography (NAH) based on equivalent source method (ESM) is an effective method for identifying sound sources. Conventional ESM focuses on relatively low frequencies and cannot provide a satisfactory solution at high frequencies.
Ming Zan +3 more
doaj +5 more sources
A High-Resolution Algorithm for Supraharmonic Analysis Based on Multiple Measurement Vectors and Bayesian Compressive Sensing [PDF]
Energies, 2019 Supraharmonics emitted by electrical equipment have caused a series of electromagnetic interference in power systems. Conventional supraharmonic analysis algorithms, e.g., discrete Fourier transform (DFT), have a relatively low frequency resolution with ...
Shuangyong Zhuang +5 more
doaj +6 more sources
Microwave NDT/NDE Through Differential Bayesian Compressive Sensing [PDF]
IEEE Open Journal of Instrumentation and MeasurementThis article deals with the nondestructive testing and evaluation (NDT/NDE) of dielectric structures through a sparseness-promoting probabilistic microwave imaging (MI) method.
Marco Salucci +5 more
doaj +3 more sources
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
doaj +3 more sources
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 +4 more sources
Complex multitask compressive sensing using Laplace priors
Electronics Letters, 2021 Most existing Bayesian compressive sensing (BCS) algorithms are developed in real numbers. This results in many difficulties in applying BCS to solve complex‐valued problems.
Qilei Zhang, Zhen Dong, Yongsheng Zhang
doaj +1 more source
Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior
IET Signal Processing, 2022 Bayesian compressive sensing (BCS) is an important sub‐class of sparse signal reconstruction algorithms. In this paper, a modified complex multitask Bayesian compressive sensing (MCMBCS) algorithm using the Laplacian scale mixture (LSM) prior is proposed.
Qilei Zhang, Lei Yu, Feng He, Yifei Ji
doaj +1 more source