Paper
28 July 2000 Sequential detection and concentration estimation of chemical vapors using range-resolved lidar with frequency-agile lasers
Author Affiliations +
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.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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Cited by 1 scholarly publication.
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KEYWORDS
LIDAR

Statistical analysis

Data modeling

Filtering (signal processing)

Detection and tracking algorithms

Algorithm development

Chemical analysis

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