From L1 Minimization to Entropy Minimization: A Novel Approach for Sparse Signal Recovery in Compressive Sensing

2018 ◽  
Author(s):  
Miguel Heredia Conde ◽  
Otmar Loffeld
Keyword(s):  
Compressive Sensing ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
L1 Minimization ◽  
Entropy Minimization ◽  
Novel Approach

Heterogeneous Bayesian compressive sensing for sparse signal recovery

2014 ◽  
Vol 8 (9) ◽  
pp. 1009-1017 ◽  
Author(s):  
Kaide Huang ◽  
Yao Guo ◽  
Xuemei Guo ◽  
Guoli Wang
Keyword(s):  
Compressive Sensing ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery

NonConvex Iteratively Reweighted Least Square Optimization in Compressive Sensing

2011 ◽  
Vol 341-342 ◽  
pp. 629-633
Author(s):  
Madhuparna Chakraborty ◽  
Alaka Barik ◽  
Ravinder Nath ◽  
Victor Dutta
Keyword(s):  
Computer Simulation ◽  
Compressive Sensing ◽  
Least Square ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Reconstruction Method ◽  
Least Square Approach ◽  
Simulation Results ◽  
Regularization Strategy

In this paper, we study a method for sparse signal recovery with the help of iteratively reweighted least square approach, which in many situations outperforms other reconstruction method mentioned in literature in a way that comparatively fewer measurements are needed for exact recovery. The algorithm given involves solving a sequence of weighted minimization for nonconvex problems where the weights for the next iteration are determined from the value of current solution. We present a number of experiments demonstrating the performance of the algorithm. The performance of the algorithm is studied via computer simulation for different number of measurements, and degree of sparsity. Also the simulation results show that improvement is achieved by incorporating regularization strategy.

scholarly journals Backtracking based Joint-Sparse Signal Recovery for Distributed Compressive Sensing

2019 ◽  
Vol 9 (2) ◽  
pp. 3919-3922
Keyword(s):  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Reconstruction Method ◽  
Joint Sparsity ◽  
Joint Reconstruction ◽  
Distributed Compressive Sensing ◽  
Efficient Recovery

In Distributed Compressive Sensing (DCS), the Joint Sparsity Model (JSM) refers to an ensemble of signals being jointly sparse. In [4], a joint reconstruction scheme was proposed using a single linear program. However, for reconstruction of any individual sparse signal using that scheme, the computational complexity is high. In this paper, we propose a dual-sparse signal reconstruction method. In the proposed method, if one signal is known apriori, then any other signal in the ensemble can be efficiently estimated using the proposed method, exploiting the dual-sparsity. Simulation results show that the proposed method provides fast and efficient recovery.

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