Abstract

Differential absorption lidar data processing traditionally assumes knowledge of the spectral dependence of the absorptivity coefficients. While this is sometimes a good assumption, it is often not in complicated collection environments where the material present is ambiguous. We present an alternative approach that estimates the vapor path-integrated concentration (CL) and absorptivity (ρ) in parallel by a processor capable of online implementation. The algorithm is based on an extended Kalman filter (EKF) for CL and a sequential maximum likelihood estimator for ρ. The state model parameters of the EKF are also estimated sequentially together with CL and ρ. The approach is illustrated on simulated and real topographic backscatter lidar data collected by the Edgewood Chemical Biological Center.

© 2007 Optical Society of America

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