Abstract
A farfield random array is implemented for sound source identification. Microphone positions are optimized, with the aid of the simulated annealing (SA) method as a supervised Monte Carlo approach, random samples of sensor position are drawn from Gaussian distribution to minimize the sidelobe maximum of the farfield beam-pattern. A two-stage localization and separation algorithm is devised on the basis of the equivalent source model (ESM). In the localization stage, the active source regions are located by using the delay-and-sum (DAS) method, followed by a parametric array localization procedure that is capable of locating sources with improved resolution. In the separation stage, source amplitude extraction is achieved by formulating an inverse problem based on the steering matrix relating the sound pressures received by the microphones and the source amplitudes. The number of sources is selected to be less than the number of microphones to render an overdetermined problem which can be solved by using the Tikhonov regularization (TIKR). Alternatively, the separation problem can be augmented into an underdetermined problem which can be solved using the compressive sensing (CS) technique. Furthermore, the acoustic variables including sound pressure, particle velocity, sound intensity, and sound power can be estimated based on ESM. Numerical and experimental results are presented to validate the proposed technique.