Stochastic Radiative Transfer Model for Contaminated Rough Surfaces: A Framework for Detection System Design
Final rept. Jan 2013-Sep 2013
ARMY EDGEWOOD CHEMICAL BIOLOGICAL CENTER APG MD
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We developed a framework to evaluate the performance of a detection system for contaminated surfaces. We employed the radiative transfer model for contaminated surfaces Ben-David and Davidson, ECBC-TR-1084, 2013 and transformed the physical model into a stochastic probability model with which detection probability and false alarms can be estimated for scenarios of interest. Our algorithm employs a data fusion approach known as a distributed binary integration system also known as a double-threshold detector, or m-out-of-n detector in order to combine the individual detection results from multiple scans over several potentially contaminated areas. With our probability model we can explore the parameter space e.g., number of measurements, time to detect, area to monitor, sparsity of the contamination, field of view, etc. and study the tradeoffs between parameters that affect the overall system detection performance. We can also use the stochastic model to set sensor requirements for a contamination scenario. We presented plots that demonstrate the interaction between parameters and an example for the detection of a potassium chlorate contaminated car with a CO2 tunable laser system.
- Chemical, Biological and Radiological Warfare