Short-term prediction method of oblique sounding ionosphere MUF based on robust Kalman filter

Guojun LI; Guangfa ZHENG; Changrong YE; Yinping ZHOU

doi:10.11959/j.issn.1000-436x.2021011

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Short-term prediction method of oblique sounding ionosphere MUF based on robust Kalman filter

Papers | 更新时间：2024-06-05

- Short-term prediction method of oblique sounding ionosphere MUF based on robust Kalman filter
- Journal on Communications Vol. 42, Issue 1, Pages: 79-86(2021)
- 作者机构：
  
  重庆邮电大学超视距可信信息传输研究所，重庆 400065
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2019YFC1511300);The National Natural Science Foundation of China(61671452);Chongqing Key Research and Development Project(cstc2017zdcy-zdyfX0011)
- DOI：10.11959/j.issn.1000-436x.2021011
  CLC： TN92
- Online First：2021-01，
  
  Published：25 January 2021
- 稿件说明：
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Guojun LI, Guangfa ZHENG, Changrong YE, et al. Short-term prediction method of oblique sounding ionosphere MUF based on robust Kalman filter[J]. Journal on Communications, 2021, 42(1): 79-86.
DOI：

Guojun LI, Guangfa ZHENG, Changrong YE, et al. Short-term prediction method of oblique sounding ionosphere MUF based on robust Kalman filter[J]. Journal on Communications, 2021, 42(1): 79-86. DOI： 10.11959/j.issn.1000-436x.2021011.

摘要

为了改善电离层频率预报准确性低、短波通信连通率差以及工程中观测野值对预报精度影响严重等问题，提出一种基于稳健卡尔曼滤波的倾斜探测电离层MUF短期预报方法。通过研究实测MUF数据的变化规律及观测误差的成因与特征，利用电离层参考模型作为先验信息，改进卡尔曼滤波模型的状态估计方程；引入代价函数机制，通过Huber-M估计实现对预报状态的量测更新，减少观测野值对预报结果的影响，提高所提方法的稳健性。仿真结果表明，所提方法能有效抑制观测野值带来的不利影响，具有较强的稳健性和稳定性。

Abstract

To improve the low accuracy of ionospheric frequency forecasting

poor HF communication connectivity

and the severe impact of observational outliers in engineering on forecast accuracy

a short-term prediction method for oblique detection ionospheric MUF based on robust Kalman filtering was proposed.By studying the variation law of the measured MUF data and the causes as well as characteristics of observation errors

and exploiting the ionospheric reference model as a priori information

the state estimation equation of the Kalman filter model was improved.The cost function mechanism was introduced to realize the measurement update of the forecast state through Huber-M estimation

which reduced the influence of the observation outliers on the forecast results and improves the robustness of the proposed method.The simulation results show that the proposed method can effectively suppress the adverse effects caused by the observation outliers and has good robustness and stability.

关键词

Keywords

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