An Operational Implementation of a CBRN Sensor-Driven Modeling Paradigm for Stochastic Event Reconstruction
DEFENCE RESEARCH AND DEVELOPMENT SUFFIELD (ALBERTA)
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This report provides a technical description of the module urbanSOURCE, which is an operational implementation of an innovative sensor-driven modeling paradigm for source reconstruction. This module permits the rapid and robust estimation of the parameters of an unknown source, using a finite number of noisy concentration measurements obtained from a sensor array. The problem is solved using a Bayesian probabilistic inferential framework in which Bayesian probability theory is used to formulate the posterior distribution for the source parameters. Three different model equations have been formulated for the likelihood function, leading to three different models for the posterior distribution of the source parameters. The application of the methodology implemented in urbanSOURCE is illustrated using real dispersion data obtained from two examples Joint Urban 2003 field experiment in Oklahoma City and European Tracer Experiment involving contaminant dispersion in highly disturbed flows over urban and complex environments, where the idealizations of horizontal homogeneity and or temporal stationarity in the flow cannot be applied to simplify the problem.
- Miscellaneous Detection and Detectors