Robust Adaptive Beamforming Based on Manifold Analysis for Flexible Conformal Array of Smart Morphing Wing Aircraft

The smart morphing wing aircraft (SMWA) is a versatile platform with real-time variable structure, making it ideal for intelligent warfare. Its crucial component is the flexible conformal array (FCA), responsible for target detection, estimation, and tracking. However, mismatches in the FCA deformation parameters can lead to degraded array performance. To ensure the beamforming capability of the FCA, it is necessary to consider the array control parameter (ACP) errors (unique for FCA), array perturbation errors, and looking direction errors in combination. Based on an analysis of the manifold characteristics of FCA steering vectors (SV), we present an adaptive beamforming algorithm that exhibits robustness to ACP errors, by utilizing the conventional constrained minimum variance optimization framework. The primary innovation of our approach lies in the use of constraints derived from the manifold of FCA's SV. We first map the initial ACP uncertainty set to manifold space (MS) and determine the minimum covering ellipsoids in MS. Then, we map the minimum covering ellipsoids in MS back to the Euclidean space to obtain the updated ACP set representation. Afterwards, we solve the convex optimization model under the updated ACP constraint set in Euclidean space to obtain the solution to the minimum variance optimization problem. Experimental results show that the proposed beamformer outperforms several reference beamformers under mixed mismatch conditions for the FCA.