Sequenced Steering Vector Estimation for Eigen-Subspace Projection-Based Robust Adaptive Beamformer

Robust adaptive beamforming (RAB) is essential in many applications to ensure signal-receiving quality when model errors exist. Eigen-subspace projection (ESP), one of the most popular RAB methods, can be used there are arbitrary errors. However, a major challenge ESP subspace selection. Traditional (TESP) treats signal as subspace; thus, source enumeration required obtain prior information. Another inherent defect its poor performance at low signal-to-noise ratios (SNRs). To overcome these drawbacks, two improved ESP-based methods proposed this study. Considering that reliable signal-of-interest steering vector needs obtained via projection, main idea underlying use sequenced estimation invert dimension estimate for an arranged eigenvector matrix. As do not require enumeration, they simple implement. Numerical examples demonstrate effectiveness and robustness terms output signal-to-interference-plus-noise ratio performance. Specifically, compared with TESP, present least 2.6 dB improvement SNRs regardless error models.