A two-stage noise source identification technique based on a farfield random parametric array.

Abstract

A farfield random array is implemented for noise source identification. Microphone positions are optimized, with the aid of the simulated annealing method. A two-stage localization and separation algorithm is devised on the basis of the equivalent source method (ESM). In the localization stage, the active source regions are located by using the delay-and-sum method, followed by a parametric localization procedure, stochastic maximum likelihood algorithm. Multidimensional nonlinear optimization is exploited in the bearing estimation process. In the separation stage, source amplitudes are extracted by formulating an inverse problem based on the preceding source bearings identified. The number of equivalent sources is selected to be less than that of microphones to render an overdetermined problem which can be readily solved by using the Tikhonov regularization. Alternatively, the separation problem can be augmented into an underdetermined problem which can be solved by using the compressive sensing technique. Traditionally, farfield arrays only give a relative distribution of source field. However, by using the proposed method, the acoustic variables including sound pressure, particle velocity, sound intensity, and sound power can be calculated based on ESM. Numerical and experimental results of several objective and subjective tests are presented.