A modified robust algorithm against large look direction error based on interference-plus-noise covariance matrix reconstruction and steering vector double estimation

Abstract

Adaptive beamforming is one of the most important aspects of array signal processing, which has been widely used in radar, sonar, mobile communications, radio astronomy, and many other fields. However, adaptive beamformers are sensitive to the model mismatch, especially when signal of interest (SOI) is presented in training data or when there are small sample snapshots. Robust adaptive beamforming based interference-plus-noise covariance matrix reconstruction (IPN-RAB), which has been proposed in the past few years, has a good performance in both low and high signal-to-noise ratio (SNR) cases, but it is ineffective because of high computational complexity. Itsupgraded version named robust algorithm for interference-plus-noise covariance matrix reconstruction (IPNCM) has much more robustness against desired signal steering vector mismatch with low computational complexity, which reconstructed interference-plus-noise covariance matrix, the central idea is that each interference steering vector is estimated as eigenvector corresponding to the largest eigenvalue obtained by utilizing the Capon spectrum estimator integrated over each interference angular sector and its power is also estimated by Capon spectrum estimator, the same idea is used to obtain SOI steering vector. However, the performance of IPNCM degrades extremely in the presence of large look direction error. Steering vector double estimation (SVDE) robust adaptive beamforming could obtain more accurate SOI steering vector by the knowledge of linear algebra and uncertainty set optimization method. In order to improve the performance against large look direction error, a modified robust algorithm based on interference-plus-noise covariance matrix reconstruction and steering vector double estimation (IPNCM-SVDE) is proposed. Simulation results demonstrate that the proposed algorithm provides stronger robustness against large look direction error with low computational complexity and outperforms other existing reconstruction-based algorithms.