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Sparse Signals Reconstruction via Adaptive Iterative Greedy Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 90 - Number 17 |
| Year of Publication: 2014 |
| Authors: Walid Osamy, Ahmed Salim, Ahmed Aziz |
10.5120/15810-4715
|
Walid Osamy, Ahmed Salim, Ahmed Aziz . Sparse Signals Reconstruction via Adaptive Iterative Greedy Algorithm. International Journal of Computer Applications. 90, 17 ( March 2014), 5-11. DOI=10.5120/15810-4715
Abstract
Compressive sensing(CS) is an emerging research field that has applications in signal processing, error correction, medical imaging, seismology, and many more other areas. CS promises to efficiently reconstruct a sparse signal vector via a much smaller number of linear measurements than its dimension. In order to improve CS reconstruction performance, this paper present a novel reconstruction greedy algorithm called the Enhanced Orthogonal Matching Pursuit (E-OMP). E-OMP falls into the general category of Two Stage Thresholding(TST)-type algorithms where it consists of consecutive forward and backward stages. During the forward stage, E-OMP depends on solving the least square problem to select columns from the measurement matrix. Furthermore, E-OMP uses a simple backtracking step to detect the previous chosen columns accuracy and then remove the false columns at each time. From simulations it is observed that E-OMP improve the reconstruction performance better than Orthogonal Matching Pursuit (OMP) and Regularized OMP (ROMP).
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