Acoustic source identification using a Generalized Weighted Inverse Beamforming technique

In the last years, acoustic source identification has gained special attention, mainly due to new environmental norms, urbanization problems and more demanding acoustic comfort expectation of consumers. From the current methods, beamforming techniques are of common use, since normally demands affordable data acquisition effort, while producing clear source identification in most of the applications. In order to improve the source identification quality, this work presents a method, based on the Generalized Inverse Beamforming, that uses a weighted pseudo-inverse approach and an optimization procedure, called Weighted Generalized Inverse Beamforming. To validate this method, a simple case of two compact sources in close vicinity in coherent radiation was investigated by numerical and experimental assessment. Weighted generalized inverse results are compared to the ones obtained by the conventional beamforming, MUltiple Signal Classification, and Generalized Inverse Beamforming. At the end, the advantages of the proposed method are outlined together with the computational effort increase compared to the Generalized Inverse Beamforming.

► The paper presents a new inverse method that have a better dynamic range and is able to split close acoustic sources. ► Numerical and experimental examples are presented. ► The method is able to identify sources even with a high SNR level.