Optimization of Sensor Resolution for Standoff Chemical Detection

Fourier transform infrared spectroscopy is a standard technique for remote detection of gaseous vapors. However, as algorithms mature and hyperspectral imaging in the longwave infrared becomes more prominent in ground based applications it is important to determine optimum parameters for detection due to potentially high data rates. One parameter, spectral resolution, is of particular interest because 1) it can be easily changed and 2) it has significant effect on the data rate. The following presents a mathematical foundation for determining the spectral resolution for vapor detection in the presence of atmospheric interferants such as water vapor and ozone. Results are validated using real-world long wave infrared hyperspectral data of several open air chemical simulant releases.