Optimal Sensor Placement for Shooter Localization Using a Genetic Algorithm

This paper proposes a method to find an optimal set of sensor positions for the shooter localization task. Here, optimality is defined in terms of best possible state estimation accuracy given by the Cramer-Rao bound. We derive an optimality criterion, present an application specific genetic algorithm to solve the optimization problem and investigate different scenarios with complete and incomplete measurement data sets and varying number of sensors. As an intermediate step we assume that the shooter state is exactly known. The results show that depending on the available measurement data set, the recommended optimal sensor positions are often unexpected. For all considered scenarios, the applied optimization approach determines the optimal positions reliably.