A Planar Array Synthesis Method Based on Deep Learning and Radiation Pattern Superposition Method

A dual-branched Convolutional Neural Network integrated with hybrid training criteria is proposed for real-time multi-beam synthesis in planar uniform antenna arrays. For higher speed in dataset generation, pattern superposition method is introduced in this article, which significantly improves data generation speed (~0.7ms per each). With a new hybrid training strategy integrating both “Data-Driven Approximation” and “Physics-Informed Approximation” criteria, flexible training while maintaining model synthesis efficiency is achieved simultaneously. Based on approaches mentioned above, the proposed dual-branched convolutional neural network enables dynamic multi-objective balance between its two branch outputs, accomplishing amplitude-phase synthesis for antenna array design. In the numerical simulation, consistent sidelobe suppression (20~40dB) is demonstrated for dual-beam steering across 10°~20° off-axis angles, with high synthesis speeds (~0.05s per each) on suitable computing platforms. In comparison experiments, the proposed method outperforms typical population-based stochastic optimization algorithms and Deep Neuron Network structure in both synthesis efficiency and pattern regularity. The model's real-time capability is verified for beam reconfiguration scenarios requiring sub-second responses, suggesting a new pathway for physics-embedded deep learning in antenna array synthesis.