【作者】 王健；

【导师】 马建国；

【作者基本信息】 天津大学 ， 微电子学与固体电子学， 2020， 博士

【摘要】 源于远程、无中继、低成本、部署灵活等独特优势,高频（HF）通信一直在军用通讯、抢险救灾、全球广播等领域发挥着极为重要的作用。目前,国内外研究的热点集中在新一代HF通信系统上,智能化是公认的关键特性。为满足未来智能HF通信系统长期规划和短期优化的选频需求,本文分别利用统计机器学习（SML）和混沌动力预测方法,建立了通信频率长期预测和短期预报模型。本文的主要内容及重要贡献如下:1.针对通信信道关键参数——电离层F2层临界频率（f_oF₂）,基于SML方法,建立了f_oF₂月中值的亚洲区域精细化长期预测模型。该模型首次联合地磁倾角及其修正值建立了f_oF₂空间动态变化映射,并利用太阳黑子数和10.7cm射电通量建立了f_oF₂周年动态变化映射。对比国际参考电离层（IRI）的CCIR和URSI两类模型,所建模型的预测均方根误差分别下降了0.27MHz和0.23MHz,对应精度分别提升了2.90%和1.85%。2.在通信频率长期预测方面,建立了亚洲区域细粒度的最高可用频率（MUF）、最优可用频率（OWF）和最高可能频率（HPF）预测模型。该模型利用SML方法建立了MUF传输因子的精细化预测模型,通过细粒化太阳活动参数并耦合地磁活动参数建立了OWF和HPF转换因子的预测模型。对比国际电信联盟（ITU）模型,MUF、OWF和HPF的预测均方根误差分别下降了1.18MHz,1.64MHz和1.06MHz,对应精度分别提升了10.89%,15.47%和9.10%。3.为实现通信频率的短期预报,首次利用Volterra级数自适应滤波方法,提出了基于混沌理论的f_oF₂小时级动态预报模型。该模型能够在一个太阳自转周期27天训练数据的支撑下取得良好的预报结果,对比IRI的CCIR和URSI两类模型,所建模型预报均方根误差分别下降了1.66MHz和1.59MHz,对应精度分别提升了31.38%和29.97%。4.在通信频率短期预报方面,利用混沌理论建立了MUF传输因子的自适应动态预报模型,并首次提出了基于地磁坐标的改进曲面样条插值方法,完成了预报关联参数的空间重构,集成时、空两方面技术最终实现了MUF的小时级动态预报。对比ITU模型,所建模型的预报均方根误差下降了1.87MHz,对应精度提升了12.63%。更多还原

【Abstract】 Due to the unique advantages of long-distance,non-relay,low-cost and flexible-deployment,HF communication plays an extremely important role in the fields of military communication,disaster relief,global broadcasting services,etc.At present,The worldwide research interests focus on developing the next generation HF communications,where one of the key features have been recognized as intelligent in the future.In order to meet the long-term spectrum planning and short-term frequency optimization for smart HF communication,the technology architecture for selecting usable frequency is optimized.And the statistical machine learning and chaotic adaptive predicting method were introduced to develop the long-term prediction and the short-term forecast model of the usable frequency for HF communication.Based on the above-mentioned model,the regional refined long-term predictions and real-time short-term forecast results of usable frequency can be obtained.The main contributions of this thesis are as follows:1.As the basis of the long-term prediction of the usable frequency for HF communication,a model based on statistical machine learning method is proposed to improve the accuracy of predicting the monthly median ionospheric critical frequency of the F2 layer（identified as f_oF₂）,which is one of the key parameters for predicting usable frequencies for HF communication.The annual dynamic variation map of the proposed model is achieved by the two solar activity parameters of the 10.7-cm solar radio flux and sunspot number.And the geomagnetic dip latitude and its modified value are first together chosen as features of the geographical spatial variation for reconstructing spatial dynamic variation map.The proposed model can provide higher prediction accuracy for f_oF₂ over Asia.Compared with the international reference ionosphere（IRI）model with CCIR and URSI coefficients（identified as IRI-CCIR and IRI-URSI）,the root-mean-square error of the proposed model is reduced by 0.27MHz and 0.23MHz respectively,and the accuracy is improved by 2.90%and 1.85%respectively.2.As to the long-term prediction method of the usable frequency for HF communication,an enhanced model is proposed to provide fine granularity and higher prediction accuracy for the maximum usable frequency（MUF）,the optimum working frequency（OWF）and the highest probable frequency（HPF）over Asia.First of all,the refined mapping model of MUF propagation factor at a distance of 3000 km of the F2layer（identified as M（3000）F2）is reconstructed by using statistical machine learning method.And then the new mapping models of conversion factors of OWF-MUF and HPF-MUF are proposed by using the fine-grained solar activity parameters and coupling with two geomagnetic activity parameters.Compared with ITU recommended model,the root-mean-square errors of MUF,OWF and HPF are reduced by 1.18MHz1.64MHz and 1.06MHz respectively,and the accuracies are improved by 10.89%,15.47%and 9.10%respectively.3.To achieve short-term forecasting the usable frequency for HF communication,a chaos-based adaptive forecasting model of f_oF₂ is first proposed as an important basis.The proposed model is based on the Volterra series adaptive filtering method,which is introduced in the ionospheric field for the first time.And it can one-hour-ahead forecast f_oF₂ with high accuracy by using a small training dataset of 27 days（one solar rotation period）.Compared with IRI-CCIR and IRI-URSI model,the root-mean-square error of the proposed model is reduced by 1.66MHz and 1.59MHz respectively,and the accuracy is improved by 31.38%and 29.97%respectively.4.As to short-term forecast of the usable frequency for HF communication,a chaos-based adaptive forecasting model of M（3000）F2 is first proposed,and a spatial interpolation method based on geomagnetic coordinates is first proposed to interpolate these characteristic parameters for HF communication.In the end,a short-term dynamic forecast model of MUF is proposed by referring to the above research achievement.Compared with ITU long-term prediction model,the root-mean-square error of the proposed model is reduced by 1.87MHz,which is corresponding to the improved accuracy of 11.30%.更多还原