NonConvex Iteratively Reweighted Least Square Optimization in Compressive Sensing

Abstract:

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.

• Periodical: Advanced Materials Research (Volumes 341-342)
• Pages: 629-633
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.341-342.629
• Online since: September 2011
• Authors: Madhuparna Chakraborty, Alaka Barik, Ravinder Nath, Victor Dutta
• Keywords: Compressive Sensing (CS), Optimization, Regularization

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