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.

The localization of sound sources in urban environments is a challenging task and the self-position and self-orientation of the individual sensor nodes can usually not be assumed to be precisely known. Therefore in this paper, a method for simultaneous sensor and source localization under line-of-sight conditions in urban environment is proposed. The method is based on acoustic bearing and relative time difference of arrival measurements as well as self-position and self-orientation measurements of an asynchronous acoustic sensor network. Here, both direct wave and first-order reflections are taken into account. We derive the Cramér-Rao bound for the corresponding localization problem and compare it with results from a Monte Carlo simulation. We consider cases where the sensors provide all measurements as well as cases where parts of the measurements fail, resulting in sub-measurement data sets. Here, a focus is on scenarios with fully unknown sensor states. Depending on the measurement data set under consideration, the initial self-position measurement is improved by taking into account the acoustic measurements, which in turn has a positive impact on the source localization accuracy in many considered cases.

This paper deals with data fusion of acoustic measurements of multiple sensors for the purpose of shooter localization. Muzzle blast and shock wave are the two impulsive sounds that occur due to supersonic gunfire. Depending on its position, a microphone array can measure a complete measurement data set, composed of two bearing angles and the TDOA between both sound waves, or an incomplete subset. In this paper a framework is created to fuse all measurements, whether complete or incomplete. For this problem, a shooter state estimator is proposed and the associated Cramér-Rao bound is derived. The estimation results are studied in Monte Carlo simulations and evaluated in comparison with the Cramér-Rao bound.