全球定位系统2026，Vol.51Issue(4)：18-29,12.DOI:10.12265/j.gnss.2026182
基于多维观测信息的动态权重融合海上长波传播时延预测方法
A dynamic weight fusion method for ASF prediction based on multi-dimensional observational data
摘要
Abstract
To address the issue that the additional secondary phase factor(ASF)in Enhanced Long-Range Navigation(eLoran)systems is highly susceptible to environmental disturbances in complex maritime environments,while conventional prediction methods exhibit limited accuracy and strong dependence on external meteorological information,this paper proposes a dynamic weight fusion method for ASF prediction based on multidimensional observational information.The proposed method utilizes receiver signal parameters as the foundation and incorporates multidimensional observational data as inputs to construct a predictive framework that integrates multiple base models.It systematically investigates the correlations among different observational features and their capabilities in characterizing ASF variations.A dynamic weighting mechanism is introduced to adaptively adjust the weights of sub-models according to their prediction errors.Furthermore,an exponentially weighted moving average(EWMA)smoothing constraint is employed to enhance model stability and robustness.Experimental results based on real-world data show that machine learning and deep learning approaches outperform traditional linear models in capturing the nonlinear evolution and temporal characteristics of ASF.The proposed fusion strategy further improves both prediction accuracy and stability.The method achieves superior performance in both time-difference prediction tasks and outperforms benchmark methods in terms of error control and correlation metrics.The results indicate that the proposed approach effectively enhances ASF prediction accuracy in complex marine environments,thereby providing significant support for high-precision positioning in eLoran systems.关键词
增强型罗兰(eLoran)/传播时延修正/附加二次相位因子(ASF)/多源数据融合/定位、导航与授时(PNT)Key words
Enhanced Long-Range Navigation(eLoran)/propagation delay correction/additional secondary phase factor(ASF)/multi-source data fusion/positioning,navigation and timing(PNT)分类
信息技术与安全科学引用本文复制引用
邸建琛,吴苗,江鹏飞,于婷译..基于多维观测信息的动态权重融合海上长波传播时延预测方法[J].全球定位系统,2026,51(4):18-29,12.基金项目
导航与时空技术国家级重点实验室自主科研课题(WXDHS202505) （WXDHS202505）
国家自然科学基金(52471386) （52471386）
