Strong-robustness minimum-variance ultrasonic beam forming method based on subspace oblique projection

Abstract

The invention relates to a strong-robustness minimum-variance ultrasonic beam forming method based on subspace oblique projection, and belongs to the technical field of ultrasonic imaging. The method comprises the following steps: performing spatial smoothing and diagonal loading processing on echo signals received by an ultrasonic array to obtain a sample covariance matrix, decomposing the characteristics of the sample covariance matrix into a signal subspace and an interference noise subspace, and constructing a transition matrix according to characteristic vectors of the signal subspace and corresponding characteristic values; calculating an oblique projection operator in combination with the expected direction vector and a pseudo-inverse matrix of the transition matrix, and projecting a weight obtained by minimum variance beam forming into a signal subspace in which the expected direction vector is located along a direction parallel to the interference noise direction matrix by using the oblique projection operator to obtain a new weight; performing weighted summation on the ultrasonic array echo signals by using the new weight to obtain a final adaptive beam forming signal; according to the method, the algorithm robustness can be remarkably enhanced, the image resolution is maintained, background noise is suppressed, and the ultrasonic imaging quality is improved on the whole.

Description

technical field [0001] The invention belongs to the technical field of ultrasonic imaging, and relates to a strong robustness minimum variance ultrasonic beam forming method based on subspace oblique projection. Background technique [0002] Ultrasound imaging is widely used in various detection fields due to its advantages of non-invasiveness, safety, convenience and low cost. The core content of ultrasonic imaging is the beamforming technology. The most widely used beamforming technology is the Delay And Sum algorithm (DAS), which delays the received echo signal according to the geometric position relationship of the array element channel. When , the data is aligned and superimposed. The traditional DAS algorithm is simple in principle and fast in imaging speed, but due to the weighting of the fixed window function, the width of the main lobe increases, the resolution is low, and the imaging quality is poor. [0003] The Minimum Variance (MV) adaptive beamforming algorit...

AI Technical Summary

Problems solved by technology

However, the minimum variance algorithm based on feature space (Eigenspace based minimum variance, ESBMV) may cause useful signals to be suppressed in complex imaging environments, resulting in internal distortion of the image.

Especially when imaging the background around strong speckles and strong scattering points, more serious black area artifacts will be produced

This is because the eigenvalues ​​corresponding to the interference and noise are larger than the eigenvalues ​​corresponding to the desired signal, which leads to the deterioration of the robustness of the minimum variance algorithm in the feature space

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