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Chinese Journal of Space Science ›› 2018, Vol. 38 ›› Issue (2): 178-187.doi: 10.11728/cjss2018.02.178

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Comparison of Short-time Prediction of f0F2 Using Kalman Filter and Autocorrelation Methodormalsize

ZHOU Yi1, ZHANG Yuannong1, JIANG Chunhua1, ZHAO Zhengyu1, LIU Jing2   

  1. 1. School of Electronic Information of Wuhan University, Wuhan 430072;
    2. Earthquake Research Institute of China Seismological Bureau, Beijing 100036
  • Received:2017-06-29 Revised:2017-11-23 Online:2018-03-15 Published:2018-03-09

Abstract:

f0F2 forecast is a significant research aspect in ionospheric study, and much work has been done to improve its prediction performance. In this paper, f0F2 data from four ionospheric observation stations (Beijing, Changchun, Qingdao and Suzhou) in 2011 are used to predict f0F2 one hour in advance with the method of Kalman filter and autocorrelation analysis. Furthermore, comparisons are carried out between ionosonde observation, the values predicted by International Ionospheric Reference Model (IRI), and the estimated values of Kalman filter and autocorrelation method. The results are described as follows. For the method of Kalman filter, its Root Mean Square Error (RMSE) and Relative Error (RE) are 0.532MHz and 8.11% respectively. The RMSE and RE values are reduced by 1.035MHz and 14.58% compared with the corresponding values obtained by IRI. In terms of autocorrelation analysis, its RMSE and RE are 0.967MHz and 11.46%, and are reduced by 1.035MHz and 11.23% compared with the corresponding values obtained by IRI. It can be concluded that the prediction precisions of above-mentioned two methods have a great promotion compared with the IRI results. Moreover, further comparisons of these three methods are carried out during a geomagnetic storm. Experimental results indicate that Kalman filter method is better than autocorrelation analysis method and IRI model, which might provide suggestions for choosing a method for short-term prediction of f0F2.

Key words: Ionosphere, f0F2 short-term prediction, Kalman filter, Autocorrelation analysis

CLC Number: