Model-Free Stochastic Localization of CBRN Releases

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.