Error analysis and parameter optimization of ionospheric autocorrelation prediction method

Ionospheric delay is an important error source in GNSS high-precision navigation and positioning applications. Through the measurement and short-term prediction of total ionospheric electron content (TEC), the positioning accuracy of GNSS single frequency users can be effectively improved, and the ionospheric effect of other radio systems can also be effectively alleviated. In the past two decades, many effective short-term prediction methods have been proposed, but none of them is absolutely leading, and the prediction accuracy of all these methods needs to be improved. In this paper, using the TEC observation data of five grid points arbitrarily selected from the Madrigal database, the autocorrelation and autoregressive moving average (ARIMA) methods are compared, and then the influence of two parameters on the prediction error in the autocorrelation prediction method is studied. Finally, an optimized parameter setting scheme is put forward for the autocorrelation prediction method. The experimental results show that: 1) The prediction error of the autocorrelation method is slightly smaller than that of the ARIMA method, and the time taken by the autocorrelation method is obviously less than that of the ARIMA method. Therefore, the comprehensive performance of the autocorrelation method is better than ARIMA method; 2) For the autocorrelation method, compared with the traditional “4+12”scheme, “3+9” scheme has better prediction performance on the whole, indicating that the ionosphere current state may be mainly related to the state of the previous three days. The relevant results can be used as a useful reference scheme for the implementation of ionospheric short-term prediction engineering.“3+9” scheme has better prediction performance on the whole, indicating that the ionosphere current state may be mainly related to the state of the previous three days. The relevant results can be used as a useful reference scheme for the implementation of ionospheric short-term prediction engineering.