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基于ARIMA误差修正预测的Klobuchar模型精化 被引量:3

Refinement of Klobuchar model based on ARIMA error correction prediction
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摘要 为了提高目前传统Klobuchar模型电离层延迟改正精度仅有50%~60%的修正率的现状,提出一种基于ARIMA误差修正预测的精化方法。采用IGS中心提供的电离层观测数据,利用双频改正模型解算的电离层VTEC值作为参考值,使用ARIMA模型对每个历元前8天Klobuchar模型和参考值之间的偏差值进行2天的短期预测,对Klobuchar模型加以偏差预测改正数进行改进。采用算例将参考值检验改进的A-Klobuchar模型的预报精度与Klobuchar模型的预报精度进行对比,结果表明:改进后的A-Klobuchar模型的精度明显高于Klobuchar模型,其总体预报精度达到了77.17%,能更显著地反映出电离层的周日变化特性。 The traditional Klobuchar model is widely used in navigation and positioning,but the ionospheric delay correction accuracy of this model is only 50%-60%.In order to improve the correction rate,a Klobuchar model refinement method based on ARIMA error correction prediction is proposed.Based on ionospheric observation data by the IGS center,and the ionospheric VTEC value with dual-frequency correction model as a reference value,ARIMA model is used to forecast the bias of the two days in the future between the Klobuchar model and the dual-frequency correction model based on the bias of the former eight days.The Klobuchar model is improved by correction prediction with bias.Finally,the reference values are used to test the prediction accuracy of the improved A-Klobuchar model and Klobuchar model.The accuracy of the improved A-Klobuchar model is obviously superior to the Klobuchar model,and the overall forecasting accuracy reaches 77.17%,better diurnal variation characteristics of the ionosphere.
作者 杨芸珍 刘立龙 黄良珂 周威 万庆同 YANG Yun-zhen;LIU Li-long;HUANG Liang-ke;ZHOU Wei;WAN Qing-tong(College of Geomatics and Geoinformation,Guilin University of Technology,Guilin 541006,China;Guangxi Key Laboratory of Spatial Information and Geomatics,Guilin University of Technology,Guilin 541006,China)
出处 《桂林理工大学学报》 CAS 北大核心 2020年第3期551-556,共6页 Journal of Guilin University of Technology
基金 国家自然科学基金项目(41664002,41704027) 广西自然科学基金项目(2018GXNSFAA294045,2017GXNSFDA 198016,2017GXNSFBA198139) 广西空间信息与测绘重点实验室项目(16-380-25-01) 广西“八桂学者”岗位专项经费项目。
关键词 电离层延迟 ARIMA模型 KLOBUCHAR模型 偏差预测 ionosphere delay ARIMA model Klobuchar model error prediction
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