A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing
In the experiment of inertial confinement fusion, soft X-ray spectrum unfolding can provide important information to optimize the design of the laser and target. As the laser beams increase, there are limited locations for installing detection channels to obtain measurements, and the soft X-ray spec...
| Main Authors: | , , , , , |
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| Format: | Article |
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MDPI AG
2018-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/11/3725 |
| _version_ | 1811298411194875904 |
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| author | Nan Xia Yunbao Huang Haiyan Li Pu Li Kefeng Wang Feng Wang |
| author_facet | Nan Xia Yunbao Huang Haiyan Li Pu Li Kefeng Wang Feng Wang |
| author_sort | Nan Xia |
| collection | DOAJ |
| description | In the experiment of inertial confinement fusion, soft X-ray spectrum unfolding can provide important information to optimize the design of the laser and target. As the laser beams increase, there are limited locations for installing detection channels to obtain measurements, and the soft X-ray spectrum can be difficult to recover. In this paper, a novel recovery method of soft X-ray spectrum unfolding based on compressive sensing is proposed, in which (1) the spectrum recovery is formulated as a problem of accurate signal recovery from very few measurements (i.e., compressive sensing), and (2) the proper basis atoms are selected adaptively over a Legendre orthogonal basis dictionary with a large size and Lasso regression in the sense of ℓ1 norm, which enables the spectrum to be accurately recovered with little measured data from the limited detection channels. Finally, the presented approach is validated with experimental data. The results show that it can still achieve comparable accuracy from only 8 spectrometer detection channels as it has previously done from 14 detection channels. This means that the presented approach is capable of recovering spectrum from the data of limited detection channels, and it can be used to save more space for other detectors. |
| first_indexed | 2024-04-13T06:18:53Z |
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| id | doaj.art-4951882f48b84ec1b32bbfb7ffecc8ab |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T06:18:53Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-4951882f48b84ec1b32bbfb7ffecc8ab2022-12-22T02:58:43ZengMDPI AGSensors1424-82202018-11-011811372510.3390/s18113725s18113725A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive SensingNan Xia0Yunbao Huang1Haiyan Li2Pu Li3Kefeng Wang4Feng Wang5Provincial Key Laboratory of Computer Integrated Manufacturing, Guangdong University of Technology, Guangzhou 510006, ChinaProvincial Key Laboratory of Computer Integrated Manufacturing, Guangdong University of Technology, Guangzhou 510006, ChinaProvincial Key Laboratory of Computer Integrated Manufacturing, Guangdong University of Technology, Guangzhou 510006, ChinaProvincial Key Laboratory of Computer Integrated Manufacturing, Guangdong University of Technology, Guangzhou 510006, ChinaProvincial Key Laboratory of Computer Integrated Manufacturing, Guangdong University of Technology, Guangzhou 510006, ChinaLaser Fusion Research Center, China Academy of Engineering Physics, Mianyang 621900, ChinaIn the experiment of inertial confinement fusion, soft X-ray spectrum unfolding can provide important information to optimize the design of the laser and target. As the laser beams increase, there are limited locations for installing detection channels to obtain measurements, and the soft X-ray spectrum can be difficult to recover. In this paper, a novel recovery method of soft X-ray spectrum unfolding based on compressive sensing is proposed, in which (1) the spectrum recovery is formulated as a problem of accurate signal recovery from very few measurements (i.e., compressive sensing), and (2) the proper basis atoms are selected adaptively over a Legendre orthogonal basis dictionary with a large size and Lasso regression in the sense of ℓ1 norm, which enables the spectrum to be accurately recovered with little measured data from the limited detection channels. Finally, the presented approach is validated with experimental data. The results show that it can still achieve comparable accuracy from only 8 spectrometer detection channels as it has previously done from 14 detection channels. This means that the presented approach is capable of recovering spectrum from the data of limited detection channels, and it can be used to save more space for other detectors.https://www.mdpi.com/1424-8220/18/11/3725spectrum unfoldingcompressive sensingsparse representationlasso regressionsoft X-ray spectrometerspectral measurement |
| spellingShingle | Nan Xia Yunbao Huang Haiyan Li Pu Li Kefeng Wang Feng Wang A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing Sensors spectrum unfolding compressive sensing sparse representation lasso regression soft X-ray spectrometer spectral measurement |
| title | A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing |
| title_full | A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing |
| title_fullStr | A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing |
| title_full_unstemmed | A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing |
| title_short | A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing |
| title_sort | novel recovery method of soft x ray spectrum unfolding based on compressive sensing |
| topic | spectrum unfolding compressive sensing sparse representation lasso regression soft X-ray spectrometer spectral measurement |
| url | https://www.mdpi.com/1424-8220/18/11/3725 |
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