Two-dimensional multi-snapshot meshless compression wave beam forming sound source identification method

Abstract

The invention discloses a two-dimensional multi-snapshot meshless compression wave beam forming sound source identification method. The two-dimensional multi-snapshot meshless compression wave beam forming sound source identification method comprises the following steps: step 1, acquiring a measurement sound pressure matrix P*; step 2, solving sound pressure by virtue of iteration: 1, solving by using an SDPT3 solver in a CVX tool box of MATLAB: (shown in the description); 2, determining (l+1)th iteration regularization parameters kl+1; 3, obtaining a weight matrix Wl+1 determined by the iteration at (l+1)th; ending the iteration when a relative change between (shown in the description) of continuous twice iteration is smaller than or equal to 10<-3> or the maximal iteration times is completed; step 3, estimating a sound source DOA; and step 4, estimating sound source intensity. By adopting the two-dimensional multi-snapshot meshless compression wave beam forming sound source identification method, the DOA with relatively small space can be accurately estimated, the intensity of the sound source can be quantitatively acquired, and the resolution ratio, denoising capacity and soundsource identification precision can be improved.

Description

technical field [0001] The invention belongs to the technical field of sound field identification. Background technique [0002] Compressed beamforming based on planar microphone array measurement is an effective way to realize two-dimensional direction-of-arrival (DOA) estimation and intensity quantification of sound sources. Traditional compressed beamforming assumes that the sound source is distributed on a set of preset discrete grid points, each grid point represents an observation direction, and the sparse constraint is reflected by minimizing the distribution vector of the sound source norm. When the above assumptions are not established, the reconstruction results will be inaccurate. This problem is called base mismatch, which often occurs in practical applications. [0003] In order to fundamentally solve this problem, two-dimensional single-snapshot and multi-snapshot gridless compression beamforming strategies have been developed successively. Compared with sin...

AI Technical Summary

Problems solved by technology

When the above assumptions are not established, the reconstruction results will be inaccurate. This problem is called base mismatch, which often occurs in practical applications

[0003] In order to fundamentally solve this problem, two-dimensional single-snapshot and multi-snapshot gridless compression beamforming strategies have been developed successively. Compared with single-snapshot, multi-snapshot method is more robust, but the existing atomic norm-based Atomic NormMinimization (ANM) two-dimensional multi-snapshot gridless compression beamforming sound source identification has the defect of failing when the distance between sound sources is small

Images