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.

Imaging sensor constellation for tomographic chemical cloud mapping.

A sensor constellation capable of determining the location and detailed concentration distribution of chemical warfare agent simulant clouds has been developed and demonstrated on government test ranges. The constellation is based on the use of standoff passive multispectral infrared imaging sensors to make column density measurements through the chemical cloud from two or more locations around its periphery. A computed tomography inversion method is employed to produce a 3D concentration profile of the cloud from the 2D line density measurements.