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Volume 32 Issue 11
Dec.  2010
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Liu Ya-Xin, Zhao Rui-Zhen, Hu Shao-Hai, Jiang Chun-Hui. Regularized Adaptive Matching Pursuit Algorithm for Signal Reconstruction Based on Compressive Sensing[J]. Journal of Electronics & Information Technology, 2010, 32(11): 2713-2717. doi: 10.3724/SP.J.1146.2009.01623
Citation: Liu Ya-Xin, Zhao Rui-Zhen, Hu Shao-Hai, Jiang Chun-Hui. Regularized Adaptive Matching Pursuit Algorithm for Signal Reconstruction Based on Compressive Sensing[J]. Journal of Electronics & Information Technology, 2010, 32(11): 2713-2717. doi: 10.3724/SP.J.1146.2009.01623

Regularized Adaptive Matching Pursuit Algorithm for Signal Reconstruction Based on Compressive Sensing

doi: 10.3724/SP.J.1146.2009.01623
  • Received Date: 2009-12-22
  • Rev Recd Date: 2010-03-31
  • Publish Date: 2010-11-19
  • Compressive sensing is a novel signal sampling theory under the condition that the signal is sparse or compressible. In this case, the small amount of signal values can be reconstructed accurately when the signal is sparse or compressible. In this paper, a new Regularized Adaptive Matching Pursuit (RAMP) algorithm is presented with the idea of regularization. The proposed algorithm could control the accuracy of reconstruction by both the adaptive process which chooses the candidate set automatically and the regularization process which gets the atoms in the final support set although the sparsity of the original signal is unknown. The experimental results show that the proposed algorithm can get better reconstruction performances and it is superior to other algorithms both visually and objectively.
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