Tree-Based Backtracking Orthogonal Matching Pursuit for Sparse Signal Reconstruction
Compressed sensing (CS) is a theory which exploits the sparsity characteristic of the original signal in signal sampling and coding. By solving an optimization problem, the original sparse signal can be reconstructed accurately. In this paper, a new Tree-based Backtracking Orthogonal Matching Pursui...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/864132 |
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| _version_ | 1850212345149652992 |
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| author | Yigang Cen Fangfei Wang Ruizhen Zhao Lihong Cui Lihui Cen Zhenjiang Miao Yanming Cen |
| author_facet | Yigang Cen Fangfei Wang Ruizhen Zhao Lihong Cui Lihui Cen Zhenjiang Miao Yanming Cen |
| author_sort | Yigang Cen |
| collection | DOAJ |
| description | Compressed sensing (CS) is a theory which exploits the sparsity characteristic of the original signal in signal sampling and coding. By solving an optimization problem, the original sparse signal can be reconstructed accurately. In this paper, a new Tree-based Backtracking Orthogonal Matching Pursuit (TBOMP) algorithm is presented with the idea of the tree model in wavelet domain. The algorithm can convert the wavelet tree structure to the corresponding relations of candidate atoms without any prior information of signal sparsity. Thus, the atom selection process will be more structural and the search space can be narrowed. Moreover, according to the backtracking process, the previous chosen atoms’ reliability can be detected and the unreliable atoms can be deleted at each iteration, which leads to an accurate reconstruction of the signal ultimately. Compared with other compressed sensing algorithms, simulation results show the proposed algorithm’s superior performance to that of several other OMP-type algorithms. |
| format | Article |
| id | doaj-art-8f1ec3d60dc945aca9e68639ecf3a3b3 |
| institution | OA Journals |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-8f1ec3d60dc945aca9e68639ecf3a3b32025-08-20T02:09:22ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/864132864132Tree-Based Backtracking Orthogonal Matching Pursuit for Sparse Signal ReconstructionYigang Cen0Fangfei Wang1Ruizhen Zhao2Lihong Cui3Lihui Cen4Zhenjiang Miao5Yanming Cen6School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Mathematics, Beijing University of Chemical Technology, Beijing 100029, ChinaSchool of Information Science and Engineering, Central South University, Changsha, Hunan 410083, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaPolytechnic College, Guizhou Minzu University, Guiyang, Guizhou 550025, ChinaCompressed sensing (CS) is a theory which exploits the sparsity characteristic of the original signal in signal sampling and coding. By solving an optimization problem, the original sparse signal can be reconstructed accurately. In this paper, a new Tree-based Backtracking Orthogonal Matching Pursuit (TBOMP) algorithm is presented with the idea of the tree model in wavelet domain. The algorithm can convert the wavelet tree structure to the corresponding relations of candidate atoms without any prior information of signal sparsity. Thus, the atom selection process will be more structural and the search space can be narrowed. Moreover, according to the backtracking process, the previous chosen atoms’ reliability can be detected and the unreliable atoms can be deleted at each iteration, which leads to an accurate reconstruction of the signal ultimately. Compared with other compressed sensing algorithms, simulation results show the proposed algorithm’s superior performance to that of several other OMP-type algorithms.http://dx.doi.org/10.1155/2013/864132 |
| spellingShingle | Yigang Cen Fangfei Wang Ruizhen Zhao Lihong Cui Lihui Cen Zhenjiang Miao Yanming Cen Tree-Based Backtracking Orthogonal Matching Pursuit for Sparse Signal Reconstruction Journal of Applied Mathematics |
| title | Tree-Based Backtracking Orthogonal Matching Pursuit for Sparse Signal Reconstruction |
| title_full | Tree-Based Backtracking Orthogonal Matching Pursuit for Sparse Signal Reconstruction |
| title_fullStr | Tree-Based Backtracking Orthogonal Matching Pursuit for Sparse Signal Reconstruction |
| title_full_unstemmed | Tree-Based Backtracking Orthogonal Matching Pursuit for Sparse Signal Reconstruction |
| title_short | Tree-Based Backtracking Orthogonal Matching Pursuit for Sparse Signal Reconstruction |
| title_sort | tree based backtracking orthogonal matching pursuit for sparse signal reconstruction |
| url | http://dx.doi.org/10.1155/2013/864132 |
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