Abstract:

Functional beamforming algorithm can overcome the defects of wide main lobe and high sidelobes effectively, which appears in conventional beamforming. Based on conventional beamforming algorithm and the matrix eigenvalue decomposition theory, the theoretical derivation of function beamforming algorithm is presented. Imaging maps of the given single source and incoherent sources are simulated based on the algorithmic program self-development. The results are listed as following. For single source or incoherent sources, functional beamforming could not only suppress sidelobes effectively and improve resolution slightly but also identify the weak source clearly. Functional beamforming output of main lobe will reduce even appear the distortion with the growth of exponent. Frequency response mismatch between microphones and channels of array has less impact for the functional beamforming imaging. The exponent is suggested about 16 on the basis of the beamforming outputs error curves with exponent. Furthermore, the sound source identification experiments on four incoherent sounds were carried out, which verifies the effectiveness of this method and the correctness of the developed program. The weak part of car dash panel are identified by functional beamforming. Besides the air condition inlet and the installation holes of air blower outlet-inlet pipe, the mounting hole of steering column is also identified. The method gets more comprehensive information of sound source and provides direction for the improvement of sound insulation performance.

Key words: beamforming, cross-spectra, eigenvalue decomposition, test, sound source identification