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

Low-rank and joint-sparse signal recovery using sparse Bayesian learning in a WBAN

2020 ◽  
Author(s):  
Yan-Bin Zhang ◽  
Long-Ting Huang ◽  
Yang-Qing Li ◽  
Ke-Sen He ◽  
Kai Zhang ◽  
...  
Keyword(s):  
Bayesian Learning ◽  
Low Rank ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Sparse Bayesian Learning

scholarly journals DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian Learning

2021 ◽  
Vol 13 (13) ◽  
pp. 2553
Author(s):  
Qi Liu ◽  
Xianpeng Wang ◽  
Mengxing Huang ◽  
Xiang Lan ◽  
Lu Sun
Keyword(s):  
Bayesian Learning ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Signal Recovery ◽  
Sparse Bayesian Learning ◽  
Localization Algorithms ◽  
Multiple Input ◽  
Dependent Component ◽  
Input Multiple Output ◽  
Grid Points

Due to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation caused by off-grid gaps. To tackle the aforementioned problems, an effective off-grid Sparse Bayesian Learning (SBL) method is proposed in this paper, which enables the calculation the direction of arrival (DOA) and range estimates. First of all, the angle-dependent component is split by reconstructing the received data and contributes to immediately extract rough DOA estimates with the root SBL algorithm, which, subsequently, are utilized to obtain the paired rough range estimates. Furthermore, a discrete grid is constructed by the rough DOA and range estimates, and the 2D-SBL model is proposed to optimize the rough DOA and range estimates. Moreover, the expectation-maximization (EM) algorithm is utilized to update the grid points iteratively to further eliminate the errors caused by the off-grid model. Finally, theoretical analyses and numerical simulations illustrate the effectiveness and superiority of the proposed method.

scholarly journals A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2021 ◽  
Author(s):  
Yunfei Cheng ◽  
Yalan Ye ◽  
Mengshu Hou ◽  
Wenwen He ◽  
Yunxia Li ◽  
...  
Keyword(s):  
Bayesian Learning ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Sparse Bayesian Learning ◽  
Recovery Algorithm
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