scholarly journals Adaptive Cluster Structured Sparse Bayesian Learning with Application to Compressive Reconstruction for Chirp Signals

2022 ◽  
Vol 190 ◽  
pp. 108343
Author(s):  
Qiang Wang ◽  
Chen Meng ◽  
Cheng Wang ◽  
Lu Wang
Keyword(s):  
Bayesian Learning ◽  
Sparse Bayesian Learning ◽  
Chirp Signals

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

Sparse Bayesian learning algorithm for separable dictionaries

2021 ◽  
Vol 111 ◽  
pp. 102990
Author(s):  
Andra Băltoiu ◽  
Bogdan Dumitrescu
Keyword(s):  
Bayesian Learning ◽  
Learning Algorithm ◽  
Sparse Bayesian Learning

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

Sparse Bayesian learning mechanism for research of audible noise in UHV transmission project

2010 ◽  
Author(s):  
Lin Niu ◽  
Jian-guo Zhao ◽  
Jian Yang ◽  
Feng Xie ◽  
Ke-jun Li
Keyword(s):  
Bayesian Learning ◽  
Sparse Bayesian Learning ◽  
Learning Mechanism

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

Improved DOA estimation based on real-valued array covariance using sparse Bayesian learning

2016 ◽  
Vol 129 ◽  
pp. 183-189 ◽  
Author(s):  
Yi Wang ◽  
Minglei Yang ◽  
Baixiao Chen ◽  
Zhe Xiang
Keyword(s):  
Bayesian Learning ◽  
Doa Estimation ◽  
Sparse Bayesian Learning

scholarly journals A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hanwei Liu ◽  
Yongshun Zhang ◽  
Yiduo Guo ◽  
Qiang Wang ◽  
Yifeng Wu
Keyword(s):  
Sparse Representation ◽  
Bayesian Learning ◽  
Multiple Input Multiple Output ◽  
Time Spectrum ◽  
Weight Vector ◽  
Mimo Radar ◽  
Sparse Bayesian Learning ◽  
Multiple Measurement Vectors ◽  
Input Multiple Output ◽  
Temporally Correlated

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment.

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