Robust adaptive beam forming directional pickup method based on subarray division

Abstract

The invention discloses a robust adaptive beam forming directional pickup method based on subarray division, and the method comprises the steps: carrying out the short-time Fourier transformation of a time domain signal received by a uniform area array, and obtaining a frequency domain signal; obtaining a plurality of sub-arrays for the uniform area array in a manner of uniformly having overlapped array elements; using a delay summation beam former for frequency domain signals of each subarray; according to an output result of the delay summation beam former, carrying out DOA estimation, judging whether a target signal exists in a current frame, and carrying out DOA information marking; carrying out pre-enhancement on the output signal of each sub-array by using an MVDR beam forming algorithm to obtain a pre-enhancement result; performing iterative processing according to the DOA information marking result and the pre-enhancement result to obtain an estimated noise covariance matrix; and selecting two virtual sub-arrays of a KP former, respectively using adaptive beamforming, calculating a weight coefficient by adopting a mode of mutual iteration of the virtual sub-arrays, and then obtaining final beamforming output.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a robust adaptive beamforming directional sound pickup method based on subarray division. Background technique [0002] Large-scale microphone arrays have the characteristics of multiple array elements and large apertures. These characteristics are beneficial to improve the ability of the beamformer to extract far-field target signals and suppress interference to a certain extent. However, for a data-driven adaptive beamformer, the increase in the number of microphones will lead to an increase in the dimension of the covariance matrix, and the number of snapshots required to accurately estimate the noise covariance matrix is ​​at least twice the dimension (ie, the number of microphones). When the number of snapshots is small, the covariance matrix will appear singular, resulting in a significant drop in the performance of the adaptive beamforming algorithm. At the same...

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Problems solved by technology

And using the Kronecker Product (KP) beamforming method in the microphone array to improve the robustness of the adaptive beamformer, but this method still cannot reduce the dimension of the covariance matrix during the calculation process

In practical applications, especially in large array systems with a large number of microphones, the robustness of the above beamformers is still poor when the number of snapshots is small compared to the number of elements in a large microphone array

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