Abstract

Remote chemical analysis by fluorescence lidar is limited by the problems of background light, multiple-component fluorescence, and atmospheric spectral attenuation. We modeled the detection of chemical compounds from simulated fluorescence-lidar returns by means of rank annihilation-factor analysis (RAFA). RAFA is a set of algorithms for analyzing excitation-emission matrices (EEM). A spectral background consisting of daylight and fluorescence from fly ash was used in our simulation. The target substance to be detected was NO₂. The simulated lidar returns were calculated as a function of range, and they included both photon noise and atmospheric extinction. A RAFA detection algorithm was applied to both background-only returns and to background-plus-NO₂ returns to determine how the background limits the range at which the components in the mixture could be distinguished. We found that the combination of daylight and photon noise severely limited the RAFA detection algorithm.

© 1990 Optical Society of America