Abstract
Effective prediction of ionospheric total electron content (TEC) is very important for Global Navigation Satellite System (GNSS) positioning and other related applications. This paper proposes an ionospheric TEC prediction method using the nonlinear autoregressive with exogenous input (NARX) neural network, which uses previous TEC data and external time parameter inputs to establish a TEC prediction model. During the years of different solar activities, 12 datasets of 3 stations with different latitudes are used for experiments. Each dataset uses the first 120 days for training and the next 20 days for testing. For each test dataset, a sliding window strategy is adopted in the prediction process, wherein the TEC of future 2 days are predicted by the true TEC values of the previous 2 days. The results show that in the year with active solar activity (2011), the TEC prediction with the NARX network can improve the accuracy by 32.3% and 43.5%, compared with the autoregressive integrated moving average (ARIMA) model and the 2-day predicted TEC product, named C2PG. While in the year with calm solar activity (2017), the prediction accuracy can be improved by 20.7% and 22.7%.
Highlights
In Global Navigation Satellite System (GNSS) applications, the delay encountered by satellite signals passing through the ionosphere is one of the main errors, which greatly restricts the performance of GNSS high-precision positioning [2,3,4]. us, effective prediction of Total electron content (TEC) has always been very important for GNSS positioning and other related applications
A TEC prediction method based on the nonlinear autoregressive with exogenous input (NARX) network is proposed
According to the periodic change of the ionosphere, the TEC information during a period of time is processed as a time series. e TEC series are treated as the inputs and outputs in the NARX at the meantime, and the time parameters are the exogenous inputs including hour, DOY, and season
Summary
Us, effective prediction of TEC has always been very important for GNSS positioning and other related applications For this problem, there are several commonly used methods including the International Reference Ionosphere model (IRI) [5], time series analysis [6], and artificial neural network [7, 8]. E nonlinear autoregressive with exogenous input (NARX) is a kind of recurrent neural network (RNN) [17], which predicts future values from past values of the time series and second time series as inputs. Zheng et al [16] proposed an ionospheric foF2 prediction method, which combined the back propagation neural network (BPNN) and IRI2012 model. According to the previous TEC data and exogenous time parameters, the NARX is used for modeling for single-point TEC time series prediction and obtaining 2-day predicted TEC in advance
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