LAUSR

Robustness is of great importance in array beamforming. With the purpose of improving the robustness of the array beamforming, methods using tensor operations are explored in this paper. Specifically, a higher-dimension tensor decomposition method to construct minimum variance distortionless response model (TD-MVDR) is proposed under the assumption that the polarization sensitive array enjoys the multilinear translation invariant property. Whereafter, the proposed TD-MVDR algorithm is incorporated into the improved conjugate gradient least squares method called TD-ICGLS to obtain a better robustness. Considering that the degradation caused by the presence of the random steering vector mismatches, we derive a diagonal loading model for TD-ICGLS to improve the robustness of it. Moreover, a method for determining the loading level is put forward as the key step for the proposed robust tensor beamformer. Results demonstrate that the proposed diagonal loading TD-ICGLS beamformer yields more robust performance than existing matrix-based solutions, such as global beamforming, while operating in a challenging scenario where the signal-of-interest power approaches the jamming power. Meanwhile, an improvement of the computational complexity in terms of TD-ICGLS is noteworthy.