稀疏共形阵列天线方向图综合

提出一种基于多任务学习的共形阵列天线稀疏优化方法.该方法在考虑单元方向图的全局旋转变换和单元极化状态差异的情况下，建立了共形阵列天线导向矢量模型，结合多任务学习框架，以均匀分布共形阵列天线同一平面上阵元的方向图作为目标任务进行学习，通过稀疏向量支撑区的识别，将欠定的阵列流形矩阵方程转换为超定的特征矩阵方程进行求解，在实现阵列方向图逼近的前提下，建立了共形阵列天线阵元激励与位置联合稀疏优化的多任务学习模型.通过分块坐标下降法对稀布共形阵列天线多任务学习模型进行求解，实现了共形阵列天线的稀疏优化布阵.理论分析与实验仿真证明，该方法能有效减少共形阵的单元数量，简化共形阵列天线结构，获得与均匀分布的共形阵列天线性能一致的天线方向图，解决了稀疏共形阵列天线方向图综合优化设计难题.

Synthesis of Sparse Conformal Array Antennas Pattern

A new method based on multi-task learning for the design of sparse conformal array is presented.Considering the different orientations of antennas and variable curvature radius of the platform,array manifold model for 3D conformal array is established.Combined with the framework of multi-task learning (MTL),the pattern of uniform conformal array antenna scatting on a cone aperture is studied.In accordance with the identified sparse vector supporting area,the underdetermined steering vector matrix equation is transformed into the over-determined conversion feature matrix equation.The synthesis performance comparisons with matrix pencil method and genetic algorithm are performed to demonstrate the superiority of the proposed algorithm over traditional algorithm in the literature.And a sparse conformal array antennas design with favorable pattern matching is obtained by MTL method.