Modified robust virtual array transformation null broadening beamforming approach

Yu Zhao; Bin Yang; Jinlei Zhang; Chao Zhang

doi:10.1117/12.2660072

3 February 2023 Modified robust virtual array transformation null broadening beamforming approach

Yu Zhao, Bin Yang, Jinlei Zhang, Chao Zhang

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125112Z (2023) https://doi.org/10.1117/12.2660072
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

The virtual antenna array technology focuses on the methods of transforming the real antenna array into virtual antenna array. Virtual array transformation (VAT) can be used to realize virtual antenna array beamforming. The degrees of freedom of antenna array can be increased and more interference is inhibited. The robustness of the VAT beamforming against jammer motion can be improved by forming broad nulls. The performance of the VAT null broadening beamforming is better than that of the conventional VAT beamforming. A modified beamforming approach is proposed, and the performance of the VAT null broadening beamforming can be further improved. The reference position of the virtual antenna array is adjusted. The position that is one element space away from the antenna array is chosen as the reference position instead of the position that is at one end side of the antenna array. Besides, the conjugate covariance matrix is introduced into the expended covariance matrix, which is constructed by the Kronecker product of the covariance matrix of virtual array and its conjugate. According to theoretical analysis, more information can be obtained by the modified approach, so the performance of VAT null broadening beamforming can be improved.

Citation Download Citation

Yu Zhao, Bin Yang, Jinlei Zhang, and Chao Zhang "Modified robust virtual array transformation null broadening beamforming approach", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125112Z (3 February 2023); https://doi.org/10.1117/12.2660072

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