电波科学学报2026，Vol.41Issue(1)：17-41,25.DOI:10.12265/j.cjors.2025229
对流层波导研究进展及人工智能应用综述
Review on research progress of tropospheric duct and applications of artificial intelligence
摘要
Abstract
In recent years,the interdisciplinary deep integration of artificial intelligence(AI)technology with tropospheric duct research has been triggering a paradigm shift in the field of tropospheric radio wave propagation.This integration compels researchers to re-examine traditional modeling and sensing methods,fostering the vigorous development of two key research directions:"intelligent inversion prediction of tropospheric ducts"and"intelligent forecasting of tropospheric ducts".Leveraging its powerful capabilities in nonlinear fitting and generalization,AI has gradually emerged as an efficient"intelligent detector and propagation simulator"for atmospheric ducts,providing adaptive environmental awareness and decision-making support for systems such as over-the-horizon communication,radar detection,and radio monitoring.Conversely,the unique channel propagation mechanisms of atmospheric ducts offer a natural testing ground for computational and sensing models based on the physics of electromagnetic waves,forming a beneficial complement to numerical computational methods.From a unified perspective,this article systematically reviews the fundamental concepts of radio wave propagation in tropospheric ducts,theoretical models,and research progress at the intersection of AI and tropospheric atmospheric ducts.In the aspect of"intelligent inversion prediction",it focuses on the applications of AI technologies(particularly deep learning)in optimizing duct parameter inversion,achieving accurate duct type identification,constructing propagation prediction models,and revealing implicit relationships with meteorological elements.In the aspect of"intelligent forecasting",the discussion centers on the application potential of AI in spatiotemporal refined modeling and multi-scale spatiotemporal prediction of ducts.Finally,the article summarizes current practical challenges,including data quality and scarcity,insufficient model generalization capability,and real-time deployment bottlenecks,and outlines promising frontier directions such as pan-domain dynamic sensing,end-to-end intelligent systems,deep learning generative models,and transfer learning.关键词
大气波导/反演预测/波导预报/信道传播/人工智能(AI)/深度学习Key words
atmospheric duct/inversion prediction/duct forecasting/channel propagation/artificial intelligence/deep learning分类
信息技术与安全科学引用本文复制引用
吴佳静,李清亮,魏志强,彭怀云,崔铁军..对流层波导研究进展及人工智能应用综述[J].电波科学学报,2026,41(1):17-41,25.基金项目
国家自然科学基金(62402462) （62402462）
河南省区域联合青年科学家基金(235200810061)National Natural Science Foundation of China(62402462) （235200810061）
Henan Provincial Regional Joint Youth Scientists Fund Project(235200810061) （235200810061）
