Abstract
An autoregressive moving average neural network (ARMANN) model is applied to predict IGS real time service corrections. ARMA coefficients are determined by applying a neural network to IGS02 orbit/clock corrections. Other than the ARMANN, the polynomial and ARMA models are tested for comparison. An optimal order of each model is determined by fitting the model to the correction data. The data fitting period for training the models is 60 min. and the prediction period is 30 min. The polynomial model is good for the fitting but bad for the prediction. The ARMA and ARMANN have a similar level of accuracies, but the RMS error of the ARMANN is smaller than that of the ARMA. The RMS error of the ARMANN is 0.046 m for the 3D orbit correction and 0.070 m for the clock correction. The difference between the ARMA and ARMANN models becomes significant as the prediction time is increased.
Highlights
International GNSS Service (IGS) has been providing orbit and clock corrections for global navigation satellite system (GNSS) navigation messages
Other than the autoregressive moving average neural network (ARMANN), the polynomial and autoregressive moving average (ARMA) models are tested for comparison
An optimal order of each model is determined by fitting the model to the correction data
Summary
International GNSS Service (IGS) has been providing orbit and clock corrections for global navigation satellite system (GNSS) navigation messages. Since an RTS correction is a function of time, it can be modeled and predicted using an autoregressive moving average (ARMA) model. The ARMA is one of the most widely used prediction methods for time series problems. A neural network (NN) can be used for time series prediction problems. Several studies have validated prediction problems based on an NN for the linear and nonlinear modeling of time series. Jwo et al [3] applied a back-propagation neural network (BPNN) and a general regression neural network (GRNN) to predict differential GPS (DGPS) pseudorange corrections. Indriyatmoko et al [11] applied AR and ARMA based back-propagation neural network (BPNN) to predict DGPS carrier phase corrections. Refan and Dameshghi [12] applied ARMANN, recurrent neural network (RNN), and evolutionary neural network (ENN) to predict GPS ephemeris errors. An optimal order of each model is determined by fitting the model to the correction data
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