Widely Linear Adaptive Beamforming Algorithm Based on Minimum Sensitivity and Eigenspace

The conventional minimum variance distortionless response (MVDR) beamformer becomes suboptimal for the noncircular signals, and deteriorates when the training samples are limited. The eigenspace-based (ESB) WL MVDR beamformer is proposed, which utilizes the eigenstructure of the correlation matrix to enhance the performance of the WL MVDR beamformer. Further, an eigenspace-based widely linear beamformer for noncircular signals using the minimum sensitivity criterion is proposed, which can be used for reducing the performance degradation when the dimension of the SI subspace can not be estimated correctly. Simulation results show that the proposed method has a better performance.