Abstract

Previous work¹ produced a maximum likelihood (ML) estimator for the path-integrated concentration vector, CL, for a set of N materials measured using topographic or atmospheric backscatter differential absorption lidar (DIAL) with at least N+1 wavelengths. That analysis also showed that a Neyman-Pearson-based detection algorithm for the generalized DIAL measurement could be developed using a fixed-size sample of lidar data. Although adequate for many purposes, the Neyman-Pearson detection approach with fixed sampling does not fully exploit the time series aspect of most DIAL data collection. As a first step toward utilizing this aspect, it was shown² that an adaptive Kalman filter could significantly improve the estimation of CL using a sequence of lidar measurements with little or no additional processing time. The question naturally arises as to whether a sequential approach could improve detection as well as estimation performance for a DIAL sensor.

© 1991 Optical Society of America