Active wavelength selection for mixture analysis with tunable infrared detectors
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abstract
2014 Elsevier B.V. All rights reserved. This article presents an active wavelength selection algorithm for multicomponent analysis with tunable infrared sensors. Traditional techniques for wavelength selection operate off-line; as a result, the resulting feature subset is fixed and only optimal for the specific mixtures and noise levels in the training set. To address this limitation, the proposed algorithm interleaves the wavelength-selection and sensing steps so that the feature subset adapts to information from previous measurements. At each point in the process, the algorithm maintains a pool of candidate solutions (i.e., mixtures) consistent with all past measurements, then selects the wavelength that maximizes discrimination across the pool. The algorithm uses a weighting function based on Akaike information criterion to promote parsimonious solutions and balance exploration vs. exploitation strategies. The algorithm is validated experimentally on binary mixture problems with a tunable infrared detector (Fabry-Perot interferometer), and its performance on higher-order mixtures characterized in simulation with a large spectral library. Active wavelength selection outperforms passive strategies, particularly at low signal-to-noise and foreground-background ratios, and when mixture components are similar, in which case the problem becomes ill conditioned.
