On Recovery of Block Sparse Signals via Block Compressive Sampling Matching Pursuit
Compressive sampling matching pursuit (CoSaMP) is an efficient reconstruction algorithm for sparse signal. When the signal is block sparse, i.e., the non-zero elements are presented in clusters, some block sparse reconstruction algorithms have been proposed accordingly. In this paper, we present a n...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8911442/ |
| _version_ | 1818323606251241472 |
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| author | Xiaobo Zhang Wenbo Xu Yupeng Cui Liyang Lu Jiaru Lin |
| author_facet | Xiaobo Zhang Wenbo Xu Yupeng Cui Liyang Lu Jiaru Lin |
| author_sort | Xiaobo Zhang |
| collection | DOAJ |
| description | Compressive sampling matching pursuit (CoSaMP) is an efficient reconstruction algorithm for sparse signal. When the signal is block sparse, i.e., the non-zero elements are presented in clusters, some block sparse reconstruction algorithms have been proposed accordingly. In this paper, we present a new block algorithm based on CoSaMP, called block compressive sampling matching pursuit (BlockCoSaMP). Compared with CoSaMP algorithm, the proposed algorithm shows improved performance when sparse signal is presented in block form. Restricted isometry property (RIP) of measurement matrix is an effective tool for analyzing the performance of the CS algorithm, and Block restricted isometry property (Block RIP) is the extension of traditional RIP. Based on the Block RIP, we derive the sufficient condition to guarantee the convergence of Block-CoSaMP algorithm. In addition, the number of required iterations is obtained. Finally, simulation experiments show that with the increase of block length, the performance of Block-CoSaMP algorithm approaches to that of block subspace pursuit (Block-SP) algorithm. When the block length and sparsity are small, the performance of Block-CoSaMP algorithm is better than that of the CoSaMP, l<sub>2</sub>/l<sub>1</sub> norm and block orthogonal matching pursuit (BOMP) algorithms. Especially, when compared with CoSaMP and l<sub>2</sub>/l<sub>1</sub> norm algorithms, the proposed algorithm exhibits more obvious performance gain. |
| first_indexed | 2024-12-13T11:15:22Z |
| format | Article |
| id | doaj.art-ab4fb51c3af9465eb37c3955aa37107d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:15:22Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ab4fb51c3af9465eb37c3955aa37107d2022-12-21T23:48:38ZengIEEEIEEE Access2169-35362019-01-01717555417556310.1109/ACCESS.2019.29557598911442On Recovery of Block Sparse Signals via Block Compressive Sampling Matching PursuitXiaobo Zhang0https://orcid.org/0000-0002-1909-3038Wenbo Xu1Yupeng Cui2Liyang Lu3Jiaru Lin4https://orcid.org/0000-0001-6593-4419School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaKey Lab of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaKey Lab of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaKey Lab of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaKey Lab of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaCompressive sampling matching pursuit (CoSaMP) is an efficient reconstruction algorithm for sparse signal. When the signal is block sparse, i.e., the non-zero elements are presented in clusters, some block sparse reconstruction algorithms have been proposed accordingly. In this paper, we present a new block algorithm based on CoSaMP, called block compressive sampling matching pursuit (BlockCoSaMP). Compared with CoSaMP algorithm, the proposed algorithm shows improved performance when sparse signal is presented in block form. Restricted isometry property (RIP) of measurement matrix is an effective tool for analyzing the performance of the CS algorithm, and Block restricted isometry property (Block RIP) is the extension of traditional RIP. Based on the Block RIP, we derive the sufficient condition to guarantee the convergence of Block-CoSaMP algorithm. In addition, the number of required iterations is obtained. Finally, simulation experiments show that with the increase of block length, the performance of Block-CoSaMP algorithm approaches to that of block subspace pursuit (Block-SP) algorithm. When the block length and sparsity are small, the performance of Block-CoSaMP algorithm is better than that of the CoSaMP, l<sub>2</sub>/l<sub>1</sub> norm and block orthogonal matching pursuit (BOMP) algorithms. Especially, when compared with CoSaMP and l<sub>2</sub>/l<sub>1</sub> norm algorithms, the proposed algorithm exhibits more obvious performance gain.https://ieeexplore.ieee.org/document/8911442/Compressed sensingblock sparseblock compressive sampling matching pursuitblock restricted isometry property |
| spellingShingle | Xiaobo Zhang Wenbo Xu Yupeng Cui Liyang Lu Jiaru Lin On Recovery of Block Sparse Signals via Block Compressive Sampling Matching Pursuit IEEE Access Compressed sensing block sparse block compressive sampling matching pursuit block restricted isometry property |
| title | On Recovery of Block Sparse Signals via Block Compressive Sampling Matching Pursuit |
| title_full | On Recovery of Block Sparse Signals via Block Compressive Sampling Matching Pursuit |
| title_fullStr | On Recovery of Block Sparse Signals via Block Compressive Sampling Matching Pursuit |
| title_full_unstemmed | On Recovery of Block Sparse Signals via Block Compressive Sampling Matching Pursuit |
| title_short | On Recovery of Block Sparse Signals via Block Compressive Sampling Matching Pursuit |
| title_sort | on recovery of block sparse signals via block compressive sampling matching pursuit |
| topic | Compressed sensing block sparse block compressive sampling matching pursuit block restricted isometry property |
| url | https://ieeexplore.ieee.org/document/8911442/ |
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