Real‐time filtering of data from mobile, passive remote infrared sensors with principal component models of background

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.