利用卷积长短期记忆网络预测全球电离层Ne  

Global Ionospheric Ne Prediction Using the ConvLSTM Network

作　　者：侯世敏 张剑[1] 杜剑平[1] HOU Shimin;ZHANG Jian;DU Jianping(Information System Engineering Institute,PLA Strategic Support Force Information Engineering University,Zhengzhou,Henan 450001,China)

机构地区：[1]战略支援部队信息工程大学信息系统工程学院,河南郑州450001

出　　处：《信号处理》2024年第7期1368-1376,共9页Journal of Signal Processing

摘　　要：由于电离层电子密度随时间变化,且空间分布不均匀,对不同频段的无线电波产生延缓和折射,因此电离层电子密度变化是影响短波通信、卫星通信、全球导航卫星系统和其他空间通信质量的一个主要因素,本文对全球电离层电子密度(Number of electron,Ne)的预测工作对短波通信设备三维射线实时追踪定位提供必要条件。本文采用国际电离层参考模型提供的2016年电离层Ne数据,根据数据的三维空间时间序列特征,搭建了自编码器和卷积长短期记忆(Convolutional Long Short-Term Memory Network,Conv LSTM)网络组成的网络结构,在不引入地球自转周期之外任何先验知识的条件下,对Ne数据进行深度学习并实现预测,首先通过实验对比了SGD、Adagrad、Adadelta、Adam、Adamax和Nadam六种优化算法的性能,又对比了三种预测策略的均方根误差(Root Mean Square Error, RMSE),1h-to-1h预测策略的全球平均RMSE为1.0 NEU(最大值的0.4%),1h-to-24h和24h-to-24h预测策略的全球平均RMSE为6.3 NEU(2.6%)。由实验结果得出以下结论,一是Nadam优化算法更适合电离层Ne的深度学习,二是1h预测策略的性能与之前类似的电离层TEC预测工作(RMSE高于1.5 TECU,最大值的1%)相比有竞争力,但预测时间太短且对数据的实时性要求较高,三是两种24h预测策略虽能实现长期预测但性能不理想,要实现三维空间时间序列的长期高精度预测需要进一步改善神经网络、模型结构和预测策略。The time-varying and uneven spatial distribution of electron density(number of electrons(Ne)in m3)in the ionosphere retards and refracts electromagnetic waves in different frequency bands,in particular,radio waves from Very Low Frequencies(VLFs)to Very High Frequencies(VHFs).The ionospheric electromagnetic activity is a major factor in the quality of HF communication,satellite communication,Global Navigation Satellite Systems(GNSSs),and other space communications.Globally forecasting the Ne would improve the positioning accuracy of HF communication equipment,particularly in providing necessary conditions for precise positioning of real-time 3D ray tracing.The International Reference Ionosphere(IRI)model is an empirical model based on long-term data records from ground and space observations of the ionosphere.It has undergone extensive validation and is used for a wide range of applications in science,engineering,and education.Extensive publicly available 2016 ionospheric Ne data provided by the IRI model IRI2016 is a time series with three-dimensional spatial characteristics.SHI has confirmed through experiments that the Convolutional Long Short-Term Memory (ConvLSTM) is better than a simple LSTM in handling spatiotempo-ral data owing to its ability to simultaneously utilize both spatial and temporal information of the data. Based on the high dimensional spatiotemporal features of Ne data, this study constructs a network model composed of an autoencoder and ConvLSTM to forecast a sequence of global Ne 3D maps without introducing any prior knowledge other than the Earth rotation periodicity. The encoder has three convolutional layers that reduce the spatial dimension, creating coded Ne maps, which are the inputs of ConvLSTM;subsequently, the decoder with three convolutional layers increases the out-put spatial size back to its original input size. We compare the performance of six optimization algorithms. We compare the root mean square error (RMSE) of three prediction strategies: the global mean RMSE of the 1h to

关 键 词：卷积长短期记忆网络 国际电离层参考模型 电离层 NE 预测 深度学习 

分 类 号：P352[天文地球—空间物理学]