Orthogonal-Clustering (OC) method

Table of Contents

• Overview
• Details
• Requirements
• Usage
• Troubleshooting

Overview

A Bayesian method which utilises the rich structure embedded in the sensing matrix for fast sparse signal recovery

Details

Title of paper

Structure-based Bayesian sparse reconstruction

Authors

Ahmed A. Quadeer and Tareq Y. Al-Naffouri

Requirements

1. A Windows, Unix/Linux, Macintosh machine capable of running MathWorks MATLAB software version R2011b or later. The software package may work on previous releases of MATLAB, but it was not checked for compatibility.

2. In order to compare the performance of OC with other sparse signal reconstruction algorithms, one would need to download the packages from their respective websites:

   • Fast Bayesian Matching Pursuit (FBMP) http://www2.ece.ohio-state.edu/~zinielj/fbmp/download.html.
   • Orthogonal Matching Pursuit (OMP) http://www.personal.soton.ac.uk/tb1m08/sparsify/sparsify.html.

Usage

Effect of the sparsity rate "p" on the performance of the OC method and comparison with other sparse reconstruction algorithms for the case when the sparse signal is Gaussian distributed.

• Run the script Experiment_Effect_p_Gaussian.m

Effect of the signal-to-noise ratio "SNR" on the performance of the OC method and comparison with other sparse reconstruction algorithms for the case when the sparse signal is Gaussian distributed.

• Run the script Experiment_Effect_SNR_Gaussian.m

Effect of the cluster length "L" on the performance of the OC method

• Run the script Experiment_Effect_L.m

Effect of the under-sampling ratio "us" on the performance of the OC method

• Run the script Experiment_Effect_Undersampling_ratio.m

Effect of the sparsity rate "p" on the performance of the OC method and comparison with other sparse reconstruction algorithms for the case when the sparse signal is non-Gaussian distributed.

• Run the script Experiment_Effect_p_nonGaussian.m

Effect of the signal-to-noise ratio "SNR" on the performance of the OC method and comparison with other sparse reconstruction algorithms for the case when the sparse signal is non-Gaussian distributed.

• Run the script Experiment_Effect_SNR_nonGaussian.m

Troubleshooting

For any questions or comments, please email at [email protected].

Citation

Plain text

Quadeer, Ahmed A. & Al-Naffouri, T. Y. Structure-Based Bayesian Sparse Reconstruction. IEEE Trans. Signal Process. 60, 6354–6367 (2012).

BibTeX

@article{Quadeer2012, author = {Quadeer, Ahmed A. and Al-Naffouri, T. Y.}, doi = {10.1109/TSP.2012.2215029}, issn = {1053-587X}, journal = {IEEE Trans. Signal Process.}, keywords = {Compressive Sensing,MyPublications}, month = {dec}, number = {12}, pages = {6354--6367}, title = {{Structure-Based Bayesian Sparse Reconstruction}}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6280684}, volume = {60}, year = {2012} }