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Stand-off detection of airborne chemical compounds has proven to be a useful method that is gaining popularity following technical progress. There are obvious advantages compared to in situ measurements when it comes to the security aspect and the ability to measure at locations otherwise hard to reach. However, an inherent limitation in many of the stand-off detection techniques lies in the fact that the measured signal from a chemical depends nonlinearly on the distance to the detector. Furthermore, the measured signal describes the summation of the responses from all chemicals spatially distributed in the line of sight of the instrument. In other words, the three dimensional extension of the chemical plume is converted into a two-dimensional image. Not only is important geometric information per se lost in this process, but the measured signal strength itself depends on the unknown plume distribution which implies that the interpretation of the observation data suffers from significant uncertainty. In this paper we investigate different and novel approaches to reconstruct the original three-dimensional distribution and concentration of the plume by implementation of atmospheric dispersion models and numerical retrieval methods. In particular our method does not require a priori assumptions on the three-dimensional distribution of the plume. We also strongly advocate the use of proper constraints to avoid unphysical solutions being derived (or post-process ‘adjustments’ to correct unphysical solutions). By applying such a reconstruction method, both improved and additional information is obtained from the original observation data, providing important intelligence to the analysts and decision makers.

Reconstructing chemical plumes from stand-off detection data of airborne chemicals using atmospheric dispersion models and data fusion