中国空间科学技术

中国空间科学技术 ›› 2021, Vol. 41 ›› Issue (1): 48-54.doi: 10.16708/j.cnki.1000-758X.2021.0006

• 研究探讨 • 上一篇    下一篇

Klobuchar电离层模型误差分析及预测

彭雅奇，李冲辉，王倚文，魏武雷，丁柏超，刘仰前   

  1. 1.中国直升机设计研究所天津直升机研发中心，天津300000
    2.北京理工大学宇航学院，北京100081
  • 出版日期:2021-02-25 发布日期:2021-02-02

Error analysis and prediction of Klobuchar ionospheric model 

PENG Yaqi，LI Chonghui，WANG Yiwen，WEI Wulei，DING Baichao，LIU Yangqian   

  1. 1.Tianjin Helicopter Research and Development Center, China Helicopter Research and Development Institute, 
    Tianjin300000, China
    2.School of Aerospace Engineering, Beijing Institute of Technology, Beijing100081, China
  • Published:2021-02-25 Online:2021-02-02

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摘要： 电离层延迟误差是全球导航卫星系统（global navigation satellite system，GNSS）中的重要误差源之一。目前在电离层延迟改正模型中，应用最广泛的是Klobuchar参数模型，但是该模型的改正率仅能达到60%左右，无法满足日益增长的精度需求。将国际GNSS监测评估系统（international GNSS monitoring & assessment system，iGMAS）发布的高精度电离层格网数据作为对照，对Klobuchar电离层模型误差进行计算和分析，结果发现在中纬度区域误差存在明显的周期性特征。为进一步提高Klobuchar电离层模型在中纬度区域的改正精度，建立了基于粒子群优化反向传播（back propagation，BP）神经网络的Klobuchar电离层误差预测模型,并以2019年10月的采样数据为例进行误差预测。结果表明，用该模型对中纬度区域电离层延迟提供误差补偿，可将精度提高到90%左右。

关键词: Klobuchar电离层模型, 粒子群优化, BP神经网络, 误差分析, 误差预测

Abstract:  Ionospheric delay error is one of the most important error sources in global navigation satellite system (GNSS). At present, the most widely used ionospheric delay correction model is the Klobuchar parameter model, but the correction rate of this model can only reach about 60%, which cannot meet the increasing accuracy requirements. The high-precision ionospheric grid data published by the international GNSS monitoring & assessment system (iGMAS) were used as a reference value to calculate and analyze the error of Klobuchar ionospheric model. The results show that the error in the mid latitude region has obvious periodic characteristics. To further improve the correction accuracy of the Klobuchar ionosphere model in the mid-latitude region, a Klobuchar ionospheric error prediction model was established based on particle swarm optimization back propagation (BP) neural network. Error prediction was made by taking the sample data of October 2019 as an example. The results show that accuracy can be improved to about 90% by using the model to compensate for the ionospheric delay error in the mid latitude region.

Key words: Klobuchar ionospheric model, particle swarm optimization, BP neural network, error analysis, error prediction