| Citation: | LIU Haoqiang, ZHAO Hongbo, FENG Wenquanet al. Regularized sparsity variable step-size adaptive matching pursuit algorithm for compressed sensing[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(10): 2109-2117. doi: 10.13700/j.bh.1001-5965.2016.0830(in Chinese)
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Compressed sensing (CS), which could break through the bottleneck of the Nyquist sampling theorem, makes the high resolution signal acquisition possible. Reconstruction algorithm is the key part of compressed sensing, and the iterative greedy algorithm is one of highly significant research directions. A novel iterative greedy algorithm for compressed sensing, named regularized sparsity variable step-size adaptive matching pursuit (RSVssAMP) algorithm, was proposed in this paper. The regularized idea and the variable step-size adaptive idea were utilized in the new algorithm to achieve a quick and accurate reconstruction under the condition that the sparsity of a signal was unknown. Compared with traditional greedy algorithms, RSVssAMP could reconstruct the signal without prior information of the sparsity, and it could accelerate the reconstruction speed obviously and achieve better performance by acquiring a better candidate set. The Gaussian sparse signal and discrete sparse signal were taken as trial signals, and the comparisons of reconstruction probability and time were demonstrated in this paper. The simulation results indicate that the proposed algorithm could achieve a higher reconstruction precision and take shorter time when compared with the existing greedy algorithms.
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