Model-Free Stochastic Localization of CBRN Releases

Abstract

Abstract-We present a novel two-stage methodology for locating a Chemical, Biological, Radiological, or Nuclear (CBRN) source in an urban area using a network of sensors. In contrast to earlier work, our approach does not solve an inverse dispersion problem but relies on data obtained from a simulation of the CBRN dispersion to obtain probabilistic descriptors of sensor measurements under a variety of CBRN release scenarios. At its first stage, subsequent sensor observations under nominal, CBRN event-free conditions are assumed to be independent and identically distributed and we rely on the method of types to detect a CBRN event. Conditional on such an event, subsequent sensor observations are assumed to follow a Markov process. Using composite hypothesis testing we map sensor measurements to a source location chosen out of a discrete set of possible locations. We leverage large deviation techniques to obtain a bound on the localization probability of error and propose several methodologies for fusing sensor data to arrive at a localization decision, including a distributed one. We also address the problem of optimally placing sensors to minimize the localization probability of error. Our techniques are validated numerically using two different CBRN release simulators.

Detection and localization of harmful atmospheric releases via support vector machines

“…At the same time, it is a deterministic compliment to the previously established stochastic localization technique in (Locke and Paschalidis 2013).…”

“…The work presented here provides a deterministic accompaniment to the stochastic localization approach presented in (Locke and Paschalidis 2013). There, considering that under a release, large (small) particulate concentrations observed at one time instance by a sensor are likely to be followed by similarly large (small) particulate concentrations, environmental sensor observations are modeled as a first-order Markov chain.…”

“…The SVM-based localization technique presented in this article is similar to the stochastic approach in (Locke and Paschalidis 2013) in that it relies on previously conducted Monte Carlo simulation of release scenarios to build training sets. It is also entirely sensor measurement based, which brings the added benefit of not relying on solving an inverse problem.…”

“…We conducted a comparison of the presented SVM CBRN localization methodology to the stochastic localization methodology presented in (Locke and Paschalidis 2013). Both schemes, under the right conditions and with the right sensor placements, can locate the origin of a CBRN release quite accurately.…”