Optimizing Sensor Placement in Urban Environments for Time Difference of Arrival Shooter Localization and Event Classification

This study addresses the optimization of the placement of multiple acoustic sensors for shooter localization and event classification in urban environments. Using ray casting solutions of the Eikonal equation, the shortest propagation paths in the urban model are computed for all source-receiver pairings. Subsequently, the expected Times of Arrival (TOAs) from virtual sources are used to evaluate the localization performance of a given sensor setup using a Monte Carlo approach. Similarly, the modelled signal paths are used to estimate the signal-to-noise ratio (SNR) of the source at the sensor level in order to predict the expected classification performance. Subsequently, a genetic algorithm solves the underlying optimization problem based on these performance metrics and identifies optimal sensor network configurations for shooter localization and event classification within the urban environment. The method is experimentally validated using audio data of propane gas cannon shots recorded at the French-German Research Institute of Saint-Louis.