Validation of a Sensor-Driven Modeling Paradigm for Multiple Source Reconstruction with FFT-07 Data


  1. Yee, E.
Corporate Authors
Defence R&D Canada - Suffield, Ralston ALTA (CAN)
A Bayesian probabilistic inferential framework provides a natural and logically consistent method for source reconstruction that fully utilizes the information provided by a limited number of noisy concentration data obtained from a network (or, array) of detectors. This report addresses the application of this framework to the difficult problem of estimating the parameters of an a priori unknown number of sources, using an array of detectors. To this purpose, Bayesian probability theory is used to formulate the full joint posterior probability density function for the number of sources and the parameters (e.g., location, emission rate, activation and deactivation times) that describe each source. A simulated annealing algorithm, applied in conjunction with a reversible-jump Markov chain Monte Carlo technique, is used to draw random samples from the posterior probability density function. By calculating the marginal posterior probability distribution of the number of sources from these samples, a maximum a posteriori estimate Ns for the number of sources can be obtained, and all samples of source distribution models with exactly Ns discrete sources can be used to provide best estimates for the source parameters (along with their associated uncertainties). The method is validated against a real dispersion experiment involving various combinations of multiple source releases conducted under a multinational cooperative FUsing Sensor Information from Observi

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Report Number
DRDC-SUFFIELD-TR-2009-040 — Technical Report
Date of publication
01 May 2009
Number of Pages
Electronic Document(PDF)

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