Optimal Sensor Placement for Shooter Localization within a Surveillance Area

This paper deals with optimal sensor placement for a scenario with enemy fire, where the shooter is expected to arise in a predefined surveillance area. For this purpose, an existing method that optimizes sensor positions with respect to a known and fixed shooter state is extended to a surveillance area. Here, a genetic algorithm is used to optimize the sensor positions with respect to localization accuracy given by the associated Cramer-Rao bound. Results with different numbers and types of sensors are presented, which confirm the applicability and relevance of our method. The results show that the recommended sensor placement depends significantly on the scenario's characteristics, such as size and location of the surveillance area or the available number of sensors.