Stagewise regularized orthogonal matching pursuit algorithm

Authors:
  • 吴迪吴迪WUDi
    吴迪WUDi
  • 王奎王奎民WANGKui-min
    王奎民WANGKui-min
  • 赵玉赵玉新ZHAOYu-xin
    赵玉新ZHAOYu-xin
  • 王巍王巍WANGWei
    王巍WANGWei
  • 陈立陈立娟CHENLi-juan
    陈立娟CHENLi-juan
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Abstract
A novel reconstruction algorithm (stagewise regularized orthogonal matching pursuit) was proposed to reconstruct signals without prior sparsity information. The method constructed the candidate set by designing threshold based on the residual from signal reconstruction. The extracted signal atoms from the candidate set were merged with the previous support set. When the candidate set was a null set, the atom with the greatest correlation was directly added to the support set. Finally, the refinement of signal approximation and residual updating were achieved by solving a leastsquare algorithm on the support set. The experimental results for Gaussian signal and binary signal with a length of 256 show that the probability of exact reconstruction can be reached above 90% on the conditions of signal sparsity of 50 and 40, and the reconstructing effects and reconstructing speeds are better than those of similar algorithms under the same condition of signal sparsity. This algorithm is proved to be higher processing speeds and more stabile.