An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks
Aiming at the problems of low data reconstruction accuracy in wireless sensor networks and users unable to receive accurate original signals, improvements are made on the basis of the stagewise orthogonal matching pursuit algorithm, combined with sparseness adaptation and the pre-selection strategy,...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501329221077165 |
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| _version_ | 1849306411516821504 |
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| author | Xinmiao Lu Yanwen Su Qiong Wu Yuhan Wei Jiaxu Wang |
| author_facet | Xinmiao Lu Yanwen Su Qiong Wu Yuhan Wei Jiaxu Wang |
| author_sort | Xinmiao Lu |
| collection | DOAJ |
| description | Aiming at the problems of low data reconstruction accuracy in wireless sensor networks and users unable to receive accurate original signals, improvements are made on the basis of the stagewise orthogonal matching pursuit algorithm, combined with sparseness adaptation and the pre-selection strategy, which proposes a sparsity adaptive pre-selected stagewise orthogonal matching pursuit algorithm. In the framework of the stagewise orthogonal matching pursuit algorithm, the algorithm in this article uses a combination of a fixed-value strategy and a threshold strategy to screen the candidate atom sets in two rounds to improve the accuracy of atom selection, and then according to the sparsity adaptive principle, the sparse approximation and accurate signal reconstruction are realized by the variable step size method. The simulation results show that the algorithm proposed in this article is compared with the orthogonal matching pursuit algorithm, regularized orthogonal matching pursuit algorithm, and stagewise orthogonal matching pursuit algorithm. When the sparsity is 35 < K < 45, regardless of the size of the perception matrix and the length of the signal, M = 128, N = 256 or M = 128, N = 512 are improved, and the reconstruction time is when the sparsity is 10, the fastest time between 25 and 25, that is, less than 4.5 s. It can be seen that the sparsity adaptive pre-selected stagewise orthogonal matching pursuit algorithm has better adaptive characteristics to the sparsity of the signal, which is beneficial for users to receive more accurate original signals. |
| format | Article |
| id | doaj-art-34b54cec744f4640b6c548bcff7ca0b3 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-34b54cec744f4640b6c548bcff7ca0b32025-08-20T03:55:06ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-03-011810.1177/15501329221077165An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networksXinmiao Lu0Yanwen Su1Qiong Wu2Yuhan Wei3Jiaxu Wang4School of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology, Harbin, ChinaSchool of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology, Harbin, ChinaHeilongjiang Network Space Research Center, Harbin, ChinaSchool of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology, Harbin, ChinaSchool of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology, Harbin, ChinaAiming at the problems of low data reconstruction accuracy in wireless sensor networks and users unable to receive accurate original signals, improvements are made on the basis of the stagewise orthogonal matching pursuit algorithm, combined with sparseness adaptation and the pre-selection strategy, which proposes a sparsity adaptive pre-selected stagewise orthogonal matching pursuit algorithm. In the framework of the stagewise orthogonal matching pursuit algorithm, the algorithm in this article uses a combination of a fixed-value strategy and a threshold strategy to screen the candidate atom sets in two rounds to improve the accuracy of atom selection, and then according to the sparsity adaptive principle, the sparse approximation and accurate signal reconstruction are realized by the variable step size method. The simulation results show that the algorithm proposed in this article is compared with the orthogonal matching pursuit algorithm, regularized orthogonal matching pursuit algorithm, and stagewise orthogonal matching pursuit algorithm. When the sparsity is 35 < K < 45, regardless of the size of the perception matrix and the length of the signal, M = 128, N = 256 or M = 128, N = 512 are improved, and the reconstruction time is when the sparsity is 10, the fastest time between 25 and 25, that is, less than 4.5 s. It can be seen that the sparsity adaptive pre-selected stagewise orthogonal matching pursuit algorithm has better adaptive characteristics to the sparsity of the signal, which is beneficial for users to receive more accurate original signals.https://doi.org/10.1177/15501329221077165 |
| spellingShingle | Xinmiao Lu Yanwen Su Qiong Wu Yuhan Wei Jiaxu Wang An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks International Journal of Distributed Sensor Networks |
| title | An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks |
| title_full | An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks |
| title_fullStr | An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks |
| title_full_unstemmed | An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks |
| title_short | An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks |
| title_sort | improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks |
| url | https://doi.org/10.1177/15501329221077165 |
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