A Bayesian Method for Managing Uncertainties Relating to Distributed Multistatic Sensor Search

Predicting the search effectiveness of a distributed multistatic sensor field is highly conditioned on information which is unknown and, for all practical intents, unknowable when engaged in a two-sided tactical situation. Yet, it is imperative to have a method for assessing the military value of such systems to inform decisions relating to procurement, optimal employment, and maximal military exploitation. The combination of Monte Carlo simulation methods and Bayesian fusion techniques allow for a robust approach for modeling the effects of uncertainty on the distribution of likely outcomes. Exemplar analysis for an Area Clearance and an Area Denial scenario demonstrate how a combined Monte Carlo simulation and Bayesian fusion system might be employed to account for uncertainty and the types of information products they can provide a decision-maker