Real-Time Pattern Synthesis for Large-Scale Conformal Arrays Based on Interpolation and Artificial Neural Network Method

Real-time pattern synthesis for large-scale conformal arrays has long been a challenging task. Two efficient methods for large-scale conformal arrays pattern synthesis in a real-time way are, respectively, proposed in this article, including the cubic spline interpolation (CSI)-based method and generalized regression neural network (GRNN)-based method. The purpose of the synthesis is to find a set of amplitude excitations that yields a pattern to maximize directivity at a given scanning angle with desired low sidelobe level (SLL). The synthesis problem is first transformed into a convex problem (CVX), and then, the solution to the problem is obtained by convex optimization algorithm to generate sample dataset. The CSI-based method sets the data in the sample set as interpolation points to construct interpolation functions, while the GRNN-based method uses the input–output pairs in the sample dataset to train the model offline, learning the laws of the problem. Then, the desired amplitude excitations can be potentially obtained in a real-time way, either by the well-constructed interpolation function or the well-trained GRNN model. Finally, the representative numerical validations of a 514-element conformal array are presented to assess the effectiveness of the proposed two methods. Comparison with other advanced methods in open literatures is also provided.