Progress in optimal processing of frequency-agile lidar data

Abstract

This paper reviews recent progress in methods for optimal detection and estimation of vapor concentration using data from frequency-agile lidar. Following a summary of the likelihood ratio statistical method for constructing optimal tests, the paper shows how the basic approach derived in an earlier paper can be generalized to include the use of transmitter pulse energy measurements for reducing the estimation variance due to shot-to-shot fluctuations in the pulse energy. The transmitter normalization method developed here is compared with the usual ratioing approach on simulated and actual lidar data. Finally, the paper extends the earlier fixed-size data sample likelihood ratio approach to include the time series aspect of data collection. Modeling the path-integrated concentration vector as a simple random walk process in time, the earlier maximum likelihood (ML) estimates are replaced by Kalman filter estimates. The Kalman filter estimates are compared to the unfiltered ML estimates on vapor chamber data.