Automobile noise source acoustic imaging method based on array element random uniform distribution spherical array deconvolution beam forming

Abstract

The invention discloses an automobile noise source acoustic imaging method based on array element random uniform distribution spherical array deconvolution beam forming. The method comprises the stepsof 1, generating a complex sound pressure data matrix p by utilizing sound signals; 2, calculating a cross-spectrum matrix C of the spherical array; 3, dividing a sound source into S observation gridpoints; 4, obtaining a spatial spectrum b; 5, generating a transfer matrix G from S observation grid points to the spherical array; 6, generating a central point propagation function vector psfcentre; 7, generating a Gaussian regularization filtering function vector psi; 8, obtaining an optimal solution of the sound source distribution vector q; 9, solving sound source distribution qJ through iteration; and 10, obtaining a sound source distribution image. According to the invention, high-resolution visual imaging is carried out on spatial distribution of main noise sources of an automobile, and positioning information and relative intensity of the main noise source and the secondary noise source can be accurately obtained from an image.

Description

technical field [0001] The invention belongs to the technical field of acoustic imaging of automobile noise sources; in particular, it relates to an acoustic imaging method of automobile noise sources based on deconvolution beamforming of randomly and uniformly distributed array elements. Background technique [0002] Beamforming technology is currently the most common method for locating noise sources. It has been widely used due to its high positioning accuracy, simple calculation process, fast speed, and high mid- and high-frequency spatial resolution. However, for a measurement array with a certain aperture, the traditional beamforming algorithm will make the spatial resolution of the noise source limited by the Rayleigh criterion. In order to obtain a higher spatial resolution, it is necessary to shorten the measurement distance, expand the array aperture or increase the analysis frequency, but in practical applications, it is often difficult to achieve good measurement...

AI Technical Summary

Problems solved by technology

However, in practical applications, the range of the sound field to be measured is often very large, which may be composed of many sound source radiations distributed in the entire three-dimensional space. One-dimensional and two-dimensional arrays need to move the position of the microphone array many times to realize the entire sound field. The identification and positioning of the sound source in the middle will increase the data measurement workload of the entire measurement process, which is very cumbersome. At the same time, the correlation between multiple measurement data cannot be guaranteed, and the test error is relatively large.

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