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.

中文翻译：

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基于流形分析的智能变形机翼柔性共形阵列鲁棒自适应波束形成


智能变形翼飞机（SMWA）是一种具有实时可变结构的多功能平台，非常适合智能战争。其关键组件是柔性共形阵列（FCA），负责目标检测、估计和跟踪。然而，FCA 变形参数的不匹配可能会导致阵列性能下降。为了保证FCA的波束形成能力，需要综合考虑阵列控制参数（ACP）误差（FCA特有）、阵列扰动误差和视向误差。基于对 FCA 转向矢量 (SV) 流形特征的分析，我们提出了一种自适应波束形成算法，该算法利用传统的约束最小方差优化框架，对 ACP 误差表现出鲁棒性。我们方法的主要创新在于使用源自 FCA SV 流形的约束。我们首先将初始 ACP 不确定性集映射到流形空间 (MS)，并确定 MS 中的最小覆盖椭球体。然后，我们将 MS 中的最小覆盖椭球体映射回欧几里德空间，以获得更新的 ACP 集表示。然后，我们在欧几里德空间中求解更新后的ACP约束集下的凸优化模型，以获得最小方差优化问题的解。实验结果表明，在 FCA 的混合失配条件下，所提出的波束形成器优于几种参考波束形成器。