Bayesian source localization in an urban environment using scattered signal distributions

Acoustic signals propagating in urban environments are influenced by rough-surface scattering, multipath reflections, and diffraction. Conventional source localization algorithms often perform poorly when these effects are present. Bayesian approaches, however, are particularly well suited to incorporating physics-based statistical models for the signal propagation. Previously, we found that the complex Wishart distribution, which describes fully saturated scattered signals across a network of receivers, can be readily employed in a Bayesian framework. This approach is very general, as it includes source triangulation and trilateration as special cases. Feasibility was initially demonstrated using simulations. In the present work, we describe an experimental demonstration of Bayesian source localization using data recorded in an urban-like environment. The experimental data were collected as part of a NATO urban acoustics-seismics experiment in Walenstadt, Switzerland, in May 2023. A network of four acoustic nodes, each with 12 microphones, was deployed and recorded emissions from a variety of sources. Initial results from the data processing and localization are described.