Robust beamforming method for multi-dimensional array tensor models
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.
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