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28 July 2000 Sequential detection and concentration estimation of chemical vapors using range-resolved lidar with frequency-agile lasers
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Abstract
This paper extends our earlier work in developing statistically optimal algorithms for estimating the range- dependent concentration of multiple vapor materials using multiwavelength frequency-agile lidar with a fixed set of wavelength bursts to the case of a time series processor that recursively updates the estimates as new data become available. The concentration estimates are used to detect the presence of one or more vapor materials by a sequential approach that accumulates likelihood in time for each range cell. A Bayesian methodology is used to construct the concentration estimates with a prior concentration smoothness constraint chosen to produce numerically stable results at longer ranges having weak signal return. The approach is illustrated on synthetic and actual field test data collected by SBCCOM.
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Russell E. Warren, Richard G. Vanderbeek, and Francis M. D'Amico "Sequential detection and concentration estimation of chemical vapors using range-resolved lidar with frequency-agile lasers", Proc. SPIE 4036, Chemical and Biological Sensing, (28 July 2000); https://doi.org/10.1117/12.394080
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