A modified reconstruction algorithm for compressed sensing with least square residual

L 1 -norm based solver has been successfully used for sparse signal reconstruction in compressed sensing. In the paper, we propose a modified method to boost the decoding performance with least-square residual for L 1 algorithms. A further performance improvement is obtained by applying iteratively...

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Bibliographic Details
Published in2015 International Conference on Communications and Signal Processing (ICCSP) pp. 0168 - 0171
Main Authors Shengqi Liu, Ronghui Zhan, Qinglin Zhai, Jiemin Hu, Jun Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2015
Subjects
Compressed sensing
Current measurement
Indexes
iteratively reweighted L 1 minimization
L 1 -norm
least square residual
Noise level
Signal processing algorithms
Target recognition
Time measurement
Yttrium
Online AccessGet full text
DOI10.1109/ICCSP.2015.7322802

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Summary:L 1 -norm based solver has been successfully used for sparse signal reconstruction in compressed sensing. In the paper, we propose a modified method to boost the decoding performance with least-square residual for L 1 algorithms. A further performance improvement is obtained by applying iteratively reweighted L 1 minimization for sparsity pattern detection. Numerical experiments show that both the proposed methods lead to a better sparsity-measurement tradeoff than their benchmark algorithms.
DOI:10.1109/ICCSP.2015.7322802