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Proceedings Paper

Sequential detection and robust estimation of vapor concentration using frequency-agile lidar time series data
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Paper Abstract

This paper extends an earlier optimal approach for frequency-agile lidar using fixed-size samples of data to include the time series aspect of data collection. The likelihood ratio test methodology for deterministic but unknown vapor concentration is replaced by a Bayesian formalism in which the path integral of vapor concentration CL evolves in time through a random walk model. The fixed- sample maximum likelihood estimates of CL derived earlier are replaced by Kalman filter estimates, and the log- likelihood ratio is generalized to a sequential test statistic written in terms of the Kalman estimates. In addition to the time series aspect, the earlier approach is generalized by (1) including the transmitted energy on a short-by-shot basis in a statistically optimum manner, (2) adding a linear slope component to the transmitter and received data models, and (3) replacing the nominal multivariate normal statistical assumption by a robust model in the Huber sensor for mitigating the effects of occasional data spikes caused by laser misfiring or EMI. The estimation and detection algorithms are compared with fixed-sample processing by the DIAL method on FAL data collected by ERDEC during vapor chamber testing at Dugway, Utah.

Paper Details

Date Published: 18 January 1999
PDF: 12 pages
Proc. SPIE 3533, Air Monitoring and Detection of Chemical and Biological Agents, (18 January 1999); doi: 10.1117/12.336851
Show Author Affiliations
Russell E. Warren, SRI International (United States)
Richard G. Vanderbeek, U.S. Army Chemical and Biological Defense Command (United States)
Francis M. D'Amico, U.S. Army Chemical and Biological Defense Command (United States)
Avishai Ben-David, Science and Technology Corp. (United States)

Published in SPIE Proceedings Vol. 3533:
Air Monitoring and Detection of Chemical and Biological Agents
Joseph Leonelli; Mark L.G. Althouse, Editor(s)

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