Extraction of Desired Spectra Using ICA Regression with DOAS

Hyeon-Ho KIM  Sung-Hwan HAN  Hyeon-Deok BAE  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E88-A   No.8   pp.2244-2246
Publication Date: 2005/08/01
Online ISSN: 
DOI: 10.1093/ietfec/e88-a.8.2244
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Measurement Technology
differential optical absorption spectroscopy,  least squares method,  linear regression,  sparsity,  independent component analysis,  

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Recently, DOAS (differential optical absorption spectroscopy) has been used for nondestructive air monitoring, in which the LS (least squares) method is used to calculate trace gas concentrations due to its computational simplicity. This paper applies the ICA (independent component analysis) method to the DOAS system of air monitoring, since the LS method is insufficient to recover the desired spectra perfectly due to sparsity characteristic. If the sparsity of reference spectra in the DOAS system imposes the assumption of independence, the ICA algorithm can be used. The proposed method is used to regress the observed spectrum on the estimates of the reference spectra. The ICA algorithm can be seen as a preprocessing method where the ICs of the references are used as the input in the regression. The performance of the proposed method is evaluated in simulation studies using synthetic data.