Artificial neural networks for predicting DGPS carrier phase and pseudorange correction |
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| Authors: | Arif Indriyatmoko Taesam Kang Young Jae Lee Gyu-In Jee Yong Beom Cho Jeongrae Kim |
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| Institution: | (1) Aerospace Engineering, Konkuk University, Seoul, South Korea;(2) Electronics Engineering, Konkuk University, Seoul, South Korea;(3) Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang, South Korea |
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| Abstract: | Artificial neural networks (ANNs) were used to predict the differential global positioning system (DGPS) pseudorange and carrier
phase correction information. Autoregressive moving average (ARMA) and autoregressive (AR) models were bounded with neural
networks to provide predictions of the correction. The neural network was employed to realize time-varying implementation.
Online training for real-time prediction of the carrier phase enhances the continuity of service of the differential correction
signals and, therefore, improves the positioning accuracy. When the correction signal from the DGPS was lost, the artificial
neural networks predicted the correction data with good accuracy for the navigation system during a limited period. Comparisons
of the prediction results using the two models are given.
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| Keywords: | Neural network DGPS Carrier phase Pseudorange ARMA AR |
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