多维阵列张量模型的鲁棒波束成形方法

doi:10.12305/j.issn.1001-506X.2024.07.01

Abstract

Abstract:

Traditional beamforming performance will significantly degrade with insufficient training samples on multi-dimensional arrays with much higher dimensions. In this paper, a tensor beamforming method that has advantages in the number of training snapshots required is introduced based on the proposed tensor model and the separability of sub-dimensions for multi-dimensional arrays. Then, for coherent interference, based on the model of the sub-dimension' s interference covariance matrix in tensor beamforming, a robust tensor beamforming method is proposed by directly estimating the interference covariance matrix. Analysis shows that the proposed method could overcome the non-homogeneous clutter environment and coherent interference and obtain a higher output signal-to-interference-noise ratio. Taking the given two-dimensional polarization-sensitive array as an example, the number of training snaps required by the proposed method is 1/3 of the traditional method, and the output signal-to-interference-to-noise ratio increases by about 2.5 dB under coherent interference scenario. The simulation results verify the effectiveness of the analysis.

Key words: robust beamforming, tensor beamforming, multi-dimensional array, coherent interference

CLC Number:

V243.2

Quanyang BI, Dan LI, Jianqiu ZHANG. Robust beamforming method for multi-dimensional array tensor models[J]. Systems Engineering and Electronics, 2024, 46(7): 2175-2183.

Figures/Tables 8

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References 28

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Robust beamforming method for multi-dimensional array tensor models

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