| REAL-TIME FILTERING OF DATA FROM MOBILE,PASSIVE REMOTE INFRARED SENSORS WITH PRINCIPAL COMPONENT MODELS OF BACKGROUND |
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| 作者姓名： | S.D.BROWN |
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| 作者单位： | S.D.BROWN Department of Chemistry，University of Delaware，Newark，DE 19716，U.S.A. |
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| 摘 要： | Real-time monitoring of pollutant levels from a mobile measuring platform requires fast,flexible dataanalysis methods.This paper reports a method for rapid analysis of passive remotely sensed infrared datawith the aid of a Kalman filter.The background spectra produced by emission from the atmosphere aremodelled at the start of the data collection sequence with a simple principal components model obtainedby eigenanalysis of the initial‘blank’data taken with the spectrometer.The species of interest areincluded in the state space model by a separate measurement of their infrared spectra.It is demonstratedthat for best filter performance in detecting the simulated pollutant species SF_6 in the atmosphere,a filtermodel 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.
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REAL-TIME FILTERING OF DATA FROM MOBILE,PASSIVE REMOTE INFRARED SENSORS WITH PRINCIPAL COMPONENT MODELS OF BACKGROUND |
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| S.D.BROWN.REAL-TIME FILTERING OF DATA FROM MOBILE,PASSIVE REMOTE INFRARED SENSORS WITH PRINCIPAL COMPONENT MODELS OF BACKGROUND[J].Journal of Geographical Sciences,1991(3). |
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| Authors: | SDBROWN |
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| Institution: | S.D.BROWN Department of Chemistry,University of Delaware,Newark,DE,U.S.A. |
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| Abstract: | |
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| Keywords: | Digital filtering Real-time analysis Kalman filtering Infrared spectroscopy Principal components regression |
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