Optimum detection of multiple vapor materials with frequency-agile lidar

Abstract

Differential absorption lidar (DIAL) is a well-established technology for estimating the concentration and its path integral CL of vapor materials using two closely spaced wavelengths. The recent development of frequency-agile lasers (FAL's) with as many as 60 wavelengths that can be rapidly scanned motivates the need for detection and estimation algorithms that are optimal for lidar employing these new sources. I derive detection and multimaterial CL estimation algorithms for FAL applications using the likelihood ratio test methodology of multivariate statistical inference theory. Three model sets of assumptions are considered with regard to the spectral properties of the backscatter from either topographic or aerosol targets. The calculations are illustrated through both simulated and actual lidar data.