Further Study on Robust Adaptive Beamforming With Optimum Diagonal Loading

TLDR

Robust adaptive beamforming seeks to mitigate uncertainties such as DOA errors, calibration imperfections, near‑far effects, mutual coupling, and modeling mismatches, often using diagonal loading, though determining its optimal level remains unclear. This study develops a robust adaptive linearly constrained minimum‑variance beamformer with an ellipsoidal steering‑vector uncertainty constraint. The approach integrates an optimum variable diagonal loading scheme that adapts loading on demand and adds a cooperative quadratic constraint on the weight‑vector norm to reduce noise enhancement at low SNR. Simulations under DOA mismatch, moving jamming, and mutual coupling demonstrate that the proposed beamformers outperform conventional and other robust designs.

Abstract

Significant effort has gone into designing robust adaptive beamforming algorithms to improve robustness against uncertainties in array manifold. These uncertainties may be caused by uncertainty in direction-of-arrival (DOA), imperfect array calibration, near-far effect, mutual coupling, and other mismatch and modeling errors. A diagonal loading technique is obligatory to fulfil the uncertainty constraint where the diagonal loading level is amended to satisfy the constrained value. The major drawback of diagonal loading techniques is that it is not clear how to get the optimum value of diagonal loading level based on the recognized level of uncertainty constraint. In this paper, an alternative realization of the robust adaptive linearly constrained minimum variance beamforming with ellipsoidal uncertainty constraint on the steering vector is developed. The diagonal loading technique is integrated into the adaptive update schemes by means of optimum variable loading technique which provides loading-on-demand mechanism rather than fixed, continuous or ad hoc loading. We additionally enrich the proposed robust adaptive beamformers by imposing a cooperative quadratic constraint on the weight vector norm to overcome noise enhancement at low SNR. Several numerical simulations with DOA mismatch, moving jamming, and mutual coupling are carried out to explore the performance of the proposed schemes and compare their performance with other traditional and robust beamformers