A modified row-sparse multiple measurement vector recovery algorithm for reconstructing multichannel EEG signals from compressive measurements

2020 ◽  
Vol 60 ◽  
pp. 101956
Author(s):  
Mohammad H. Aghababaei ◽  
Ghasem Azemi
Keyword(s):  
Eeg Signals ◽  
Multiple Measurement ◽  
Recovery Algorithm ◽  
Multiple Measurement Vector ◽  
Measurement Vector ◽  
Multichannel Eeg

scholarly journals ISAR Imaging Based on Multiple Measurement Vector Model Sparse Signal Recovery Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Junjie Feng
Keyword(s):  
Vector Model ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Negative Exponential Function ◽  
Multiple Measurement ◽  
Recovery Algorithm ◽  
Multiple Measurement Vector ◽  
Isar Imaging ◽  
Measurement Vector

A multiple measurement vector (MMV) model blocks sparse signal recovery. ISAR imaging algorithm is proposed to improve ISAR imaging quality. Firstly, the sparse imaging model is built, and block sparse signal recovery algorithm-based MMV model is applied to ISAR imaging. Then, a negative exponential function is proposed to approximately block L0 norm. The optimization solution of smoothed function is obtained by constructing a decreasing sequence. Finally, the correction steps are added to ensure the optimal solution of the block sparse signal along the fastest descent direction. Several simulations and real data simulation experiments verify the proposed algorithm has advantages in imaging time and quality.

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