A novel multiobjective optimization algorithm for sparse signal reconstruction
Sparsity and reconstruction error are two main objectives to be optimized in sparse signal reconstruction. In this paper, sparse signals are reconstructed by optimizing these two objectives simultaneously. This reconstruction method mainly consists of three steps. First, a one-dimension-dominated me...
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| Vydané v: | Signal processing Ročník 167; s. 107292 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.02.2020
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| ISSN: | 0165-1684, 1872-7557 |
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| Abstract | Sparsity and reconstruction error are two main objectives to be optimized in sparse signal reconstruction. In this paper, sparse signals are reconstructed by optimizing these two objectives simultaneously. This reconstruction method mainly consists of three steps. First, a one-dimension-dominated method is used to find a uniformly distributed optimal compromise solution set between these two objectives. Second, the Iterative Half Thresholding method is employed to improve the sparsity. Third, a robust selection method is proposed to choose a final solution from the solution set. The proposed method is compared with eight sparse reconstruction algorithms on twelve sparse test instances. Experimental results show that the proposed algorithm is able to reconstruct both noisy and noiseless sparse signals. In addition, the effectiveness of the proposed algorithm is demonstrated in practical application instances. |
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| AbstractList | Sparsity and reconstruction error are two main objectives to be optimized in sparse signal reconstruction. In this paper, sparse signals are reconstructed by optimizing these two objectives simultaneously. This reconstruction method mainly consists of three steps. First, a one-dimension-dominated method is used to find a uniformly distributed optimal compromise solution set between these two objectives. Second, the Iterative Half Thresholding method is employed to improve the sparsity. Third, a robust selection method is proposed to choose a final solution from the solution set. The proposed method is compared with eight sparse reconstruction algorithms on twelve sparse test instances. Experimental results show that the proposed algorithm is able to reconstruct both noisy and noiseless sparse signals. In addition, the effectiveness of the proposed algorithm is demonstrated in practical application instances. |
| ArticleNumber | 107292 |
| Author | Liang, Jing Yue, Caitong Zhu, Yongsheng Han, Yuhong Qu, Boyang Crisalle, Oscar D. |
| Author_xml | – sequence: 1 givenname: Caitong orcidid: 0000-0002-3362-0703 surname: Yue fullname: Yue, Caitong email: zzuyuecaitong@163.com organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 2 givenname: Jing orcidid: 0000-0003-0811-0223 surname: Liang fullname: Liang, Jing email: liangjing@zzu.edu.cn organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 3 givenname: Boyang surname: Qu fullname: Qu, Boyang email: qby1984@hotmail.com organization: School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou, China – sequence: 4 givenname: Yuhong surname: Han fullname: Han, Yuhong email: suthanyuhong@163.com organization: School of Materials Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 5 givenname: Yongsheng surname: Zhu fullname: Zhu, Yongsheng email: zhuysdy@163.com organization: School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou, China – sequence: 6 givenname: Oscar D. surname: Crisalle fullname: Crisalle, Oscar D. email: crisalle@che.ufl.edu organization: Department of Chemical Engineering, University of Florida, Florida, United States |
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| Keywords | Particle swarm optimization (PSO) Sparse reconstruction Multiobjective optimization Compress sensing |
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| SubjectTerms | Compress sensing Multiobjective optimization Particle swarm optimization (PSO) Sparse reconstruction |
| Title | A novel multiobjective optimization algorithm for sparse signal reconstruction |
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