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Real‐time filtering of data from mobile, passive remote infrared sensors with principal component models of background
Author(s) -
Brown S. D.
Publication year - 1991
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180050304
Subject(s) - principal component analysis , remote sensing , kalman filter , spectrometer , filter (signal processing) , infrared , atmosphere (unit) , environmental science , computer science , meteorology , artificial intelligence , optics , computer vision , physics , geology
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 SF 6 in the atmosphere, a filter model with two principal components describing the emission background works best. The filter ‘maps’ of SF 6 closely follow the integrated spectral intensities measured after removal of suitable backgrounds.