Detection and characterization of chemical vapor fugitive emissions by nonlinear optimal estimation: theory and simulation.

This paper addresses detection and characterization of chemical vapor fugitive emissions in a nonscattering atmosphere by processing of remotely-sensed long-wavelength infrared spectra. The analysis approach integrates a parameterized signal model based on the radiative transfer equation with a statistical model for the infrared background. The maximum likelihood model parameter values are defined as those that maximize a Bayesian posterior probability and are estimated using a Gauss-Newton algorithm. For algorithm performance evaluation we simulate observation of fugitive emissions by augmenting plume-free measured spectra with synthetic plume signatures. As plumes become optically thick, the Gauss-Newton algorithm yields significantly more accurate estimates of chemical vapor column density and significantly more favorable plume detection statistics than clutter-matched-filter-based and adaptive-subspace-detector-based plume characterization and detection.