用机器学习快速预测雷达天线尺寸，省去反复测试。
Design of Cavity Backed Slotted Antenna using Machine Learning Regression Model
- 用反射系数数据训练回归模型，反推天线尺寸。
- 可在1-8 GHz频段精准预测多频谐振特性。
- 适合军事、航空等对天线设计效率要求高的场景。
本文提出一种基于回归的机器学习模型，用于腔体缝隙天线的设计。该类天线广泛应用于军用与航空通信系统。首先利用电磁求解器生成腔体缝隙天线的初始反射系数数据，随后将其作为输入训练机器学习模型，使其能够根据给定的反射系数，在1–8 GHz宽频带范围内预测天线的物理尺寸。该方法可实现天线结构的快速优化，显著减少重复物理测试和人工调参的需求，有望大幅降低设计开发成本。所提模型还展现出在1–8 GHz范围内预测多重频率谐振的能力，验证了机器学习在先进天线设计中的潜力，可提升雷达、军用识别系统及安全通信网络等实际应用中的设计效率与精度。
原文摘要 · Abstract (English)
In this paper, a regression-based machine learning model is used for the design of cavity backed slotted antenna. This type of antenna is commonly used in military and aviation communication systems. Initial reflection coefficient data of cavity backed slotted antenna is generated using electromagnetic solver. These reflection coefficient data is then used as input for training regression-based machine learning model. The model is trained to predict the dimensions of cavity backed slotted antenna based on the input reflection coefficient for a wide frequency band varying from 1 GHz to 8 GHz. This approach allows for rapid prediction of optimal antenna configurations, reducing the need for repeated physical testing and manual adjustments, may lead to significant amount of design and development cost saving. The proposed model also demonstrates its versatility in predicting multi frequency resonance across 1 GHz to 8 GHz. Also, the proposed approach demonstrates the potential for leveraging machine learning in advanced antenna design, enhancing efficiency and accuracy in practical applications such as radar, military identification systems and secure communication networks.
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