Abstract Mutual coupling between antenna array elements causes radiation pattern distortion and performance degradation, leading to undesired signal suppression and a significant reduction in the signal-to-interference-plus-noise ratio (SINR). Adaptive array signal processing typically mitigates these distortions. However, it depends on a previous calibration of the array. Such calibration requires previous knowledge of the in-situ or realistic array manifold vector (AMV), whose estimation can be achieved through various techniques extensively explored by the antenna community over the past decades. These techniques often rely on pre-measurements or electromagnetic simulations of the array and are typically design-specific. In practical scenarios where the realistic AMV is unknown, adaptive beamforming algorithms tend to converge to distorted solutions, which deviate substantially from the performance of a calibrated system, resulting in significant SINR loss. This work addresses this challenge by proposing an algorithm that mitigates signal distortion effects without requiring prior knowledge of the realistic AMV. Also, the algorithm does not rely on a specific structure for the mutual coupling matrix (MCM), such as Toeplitz. The main idea is to combine a robust design with a technique that enforces sidelobe suppression while keeping the distortionless constraint, using additional linear constraints to the adaptive filter. As case studies, the MCMs of three uniform linear array antennas are estimated using full-wave 3D electromagnetic simulations. The results demonstrate the effectiveness of the proposed approach in significantly improving beamforming performance and SINR in scenarios where the realistic array manifold vector is unknown.
Klingelfus et al. (Thu,) studied this question.