Super-resolution localization and orientation estimation of multiple dipole sound sources: From a maximum likelihood framework to wind tunnel validation

Abstract

This paper investigates the identification of multiple dipole sound sources using sound pressures measured from a microphone array. The problem is addressed in the maximum likelihood (ML) framework, where the locations, orientations, and powers of multiple dipole sound sources are unknown parameters to be estimated. By the consistency property of ML, the estimated parameters converge to their actual values, which implies an asymptotically perfect spatial resolution, if a sufficiently high signal-to-noise ratio can be achieved. In order to reduce the dimension of the optimization problem of ML, the contribution of each dipole source to the measured pressures is assumed to be a latent variable and the ML problem is equivalently solved via the expectation–maximization (EM) algorithm, which iteratively and sequentially updates each source contribution and the associated sound source parameters. The number of sound sources can also be determined by the model selection approaches which add a penalty of model dimension to the ML objective function. The proposed method is assessed via a laboratory experiment where the sound field is produced by dipole speakers and a wind tunnel experiment where airframe aerodynamic noise is generated at a high Reynolds number. Experimental results show that the proposed method outperforms existing approaches in the sense of higher spatial resolution, more accurate localization, and the capacity to identify the orientations of multiple dipole sound sources.