Robustness is an important factor in adaptive beamforming because various mismatches
that exist in reality will lead to considerable performance degradation. In this paper, a robust …

Robust adaptive beamforming (RAB) plays a vital role in modern communications by
ensuring the reception of high-quality signals. This article proposes a deep learning …

The performance of the sample matrix inverse (SMI) beamformer degrades greatly when the
signal-to-noise ratio (SNR) increases because the signal of interest (SOI) is mistaken as …

In this work, a simple and effective robust adaptive beamforming technique is proposed for
uniform linear arrays, which is based on the power spectral estimation and uncertainty …

In this paper, a novel robust adaptive beamforming (RAB) technique based on the
covariance matrix reconstruction is proposed to solve the array model mismatch. The …

Aimed at the performance degradation of the standard Capon beamformer (SCB) when the
signal of interest (SOI) appearing in the training data under the colored noise, a novel …

The computational complexity of the conventional adaptive beamformer is relatively large,
and the performance degrades significantly due to the model mismatch errors and the …

Frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radars can generate a
range-angle two-dimensional transmit steering vector (SV), which is capable of suppressing …

In this article, we address the problem of robust adaptive beamforming in the presence of
array sensor miscalibration. We consider the use of partly calibrated linear arrays, where …

Robust adaptive beamforming (RAB) based on interference-plus-noise covariance (INC)
matrix reconstruction can experience performance degradation when model mismatch …