Optimal experimental design for sound source localization

Acoustic source localization has a variety of application areas. Especially, methods using the inverse scheme with processed measurement data captured by microphone arrays have been commonly used over the last decade.

In this poster, we propose a method for optimal experimental design for the acoustic source identification problem. Optimizing the design of experiments is important so that the uncertainty in the estimated parameters is minimized. The source that needs to be identified is sparse and infinite-dimensional, which is challenging from both mathematical and computational points of view. Our approach relies on an iteratively reweighted least-squares scheme.

This is a joint work with Barbara Kaltenbacher, Daniel Walter and Konstantin Pieper.