ISSN:
0886-9383
Keywords:
Digital filtering
;
Real-time analysis
;
Kalman filtering
;
Infrared spectroscopy
;
Principal components regression
;
Chemistry
;
Analytical Chemistry and Spectroscopy
Source:
Wiley InterScience Backfile Collection 1832-2000
Topics:
Chemistry and Pharmacology
Notes:
Real-time monitoring of pollutant levels from a mobile measuring platform requires fast, flexible data analysis methods. This paper reports a method for rapid analysis of passive remotely sensed infrared data with the aid of a Kalman filter. The background spectra produced by emission from the atmosphere are modelled at the start of the data collection sequence with a simple principal components model obtained by eigenanalysis of the initial ‘blank’ data taken with the spectrometer. The species of interest are included in the state space model by a separate measurement of their infrared spectra. It is demonstrated that for best filter performance in detecting the simulated pollutant species SF6 in the atmosphere, a filter model with two principal components describing the emission background works best. The filter ‘maps’ of SF6 closely follow the integrated spectral intensities measured after removal of suitable backgrounds.
Additional Material:
9 Ill.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1002/cem.1180050304
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