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Open AccessArticle

A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing

1
Provincial Key Laboratory of Computer Integrated Manufacturing, Guangdong University of Technology, Guangzhou 510006, China
2
School of Physics and Electrical Engineering, Shaoguan University, Shaoguan 512005, China
3
Laser Fusion Research Center, China Academy of Engineering Physics, Mianyang 621900, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3725; https://doi.org/10.3390/s18113725
Received: 11 October 2018 / Revised: 28 October 2018 / Accepted: 29 October 2018 / Published: 1 November 2018
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: spectrum unfolding; compressive sensing; sparse representation; lasso regression; soft X-ray spectrometer; spectral measurement spectrum unfolding; compressive sensing; sparse representation; lasso regression; soft X-ray spectrometer; spectral measurement
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MDPI and ACS Style

Xia, N.; Huang, Y.; Li, H.; Li, P.; Wang, K.; Wang, F. A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing. Sensors 2018, 18, 3725. https://doi.org/10.3390/s18113725

AMA Style

Xia N, Huang Y, Li H, Li P, Wang K, Wang F. A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing. Sensors. 2018; 18(11):3725. https://doi.org/10.3390/s18113725

Chicago/Turabian Style

Xia, Nan; Huang, Yunbao; Li, Haiyan; Li, Pu; Wang, Kefeng; Wang, Feng. 2018. "A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing" Sensors 18, no. 11: 3725. https://doi.org/10.3390/s18113725

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