多替代模型辅助的微波器件优化方法研究

       现代通信技术的发展，对微波滤波器和天线等射频微波器件的性能的要求逐渐提高。目前的设计和优化难点主要是由于设计参数较多、电磁仿真耗时较长而导致优化困难甚至难以得到最优解的情况。基于替代模型的优化方法在射频微波器件设计领域逐渐流行，它利用计算成本低的替代模型来指导实际的高保真电磁模型实现快速优化，对解决复杂射频微波器件的优化设计问题具有重要意义。
       本文围绕多替代模型辅助的优化方法进行了深入研究。研究了数据驱动的替代模型辅助微波滤波器的设计优化方法。提出了利用矢量拟合技术提取微波滤波器的响应特征，建立基于神经网络的替代模型，结合哈里斯鹰群优化算法实现了滤波器的优化设计；研究了物理驱动的替代模型辅助天线波束方向的设计优化方法，提出了利用天线阵因子建立替代模型作为粗糙模型，电磁仿真模型作为精细模型的方法，并结合空间映射算法实现了天线的波束方向优化；研究了结合数据驱动和物理驱动的替代模型的优化方法。提出了多替代模型辅助的优化算法框架实现了阵列天线的整体优化设计。
       本文利用四阶同轴腔腔体滤波器来验证数据驱动的替代模型优化算法。文中提出的算法与直接优化（PSO）相比，优化结果相近，优化时间减少97%，且具有较好的稳定性。利用基于仿表面等离子激元（SSPP）的阵列天线来验证物理驱动的替代模型优化算法以及多替代模型辅助的优化算法。实验结果显示前者实现了24GHz 下的指定波束方向，后者解决了多设计目标的天线阵列设计问题，如实现了在26-28GHz 时，副瓣水平接近于-20dB 等。

      With the advancement of modern communication technology, the performance requirements of RF microwave devices such as microwave filters and antennas are increasingly stringent. Due to a large number of design parameters and time-consuming electromagnetic simulation, etc., design optimization of these devices is difficult. Surrogatebased optimization methods are becoming popular in the field of RF microwave device design. These methods exploit low computational cost surrogate models to guide highfidelity electromagnetic models to achieve fast optimization. These methods are essential for solving the complex RF microwave devices design problems.
        The thesis proposes a multi-surrogate model-assisted optimization approach. Firstly, a data-driven surrogate model is introduced to assist the design of microwave filters. After extracting the response features of microwave filters using vector fitting techniques, the surrogate model is established based on neural networks. The optimal design of the filter is obtained using the Harris Hawks optimization algorithm. Secondly, a physics-based surrogate-assisted antenna beam direction design optimization method is introduced. The optimization is carried out using the space mapping technique. The space mapping technique leverages the physics-based surrogate, namely an antenna array factor formula as
a “coarse model”. The electromagnetic simulation model is a “fine model”. Finally, the data-driven and physics-based surrogate model-assisted optimization methods are combined to form the multi-surrogate-assisted optimization algorithm framework.
        A fourth-order coaxial cavity filter is used to validate the data-driven surrogate model optimization algorithm. The results show that the optimization results of the proposed algorithm are similar to direct  optimization (PSO), but with 97% reduction in optimization time and better stability. A spoof surface plasmon polariton (SSPP) array antenna is used to validate the physics-based surrogate model and the multi-surrogate-assisted optimization algorithm. The results show that the former achieves the specified beam direction at 24 GHz, and the latter solves the antenna array design problem with multiple design
objectives, such as achieving a side lobe level close to -20 dB at 26-28 GHz.

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