Artificial neural networks for predicting DGPS carrier phase and pseudorange correction

被引：0
作者：
Arif Indriyatmoko
Taesam Kang
Young Jae Lee
Gyu-In Jee
Yong Beom Cho
Jeongrae Kim
机构：
[1] Konkuk University,Aerospace Engineering
[2] Konkuk University,Electronics Engineering
[3] Korea Aerospace University,Aerospace and Mechanical Engineering
来源：
GPS Solutions | 2008年 / 12卷
关键词：
Neural network; DGPS; Carrier phase; Pseudorange; ARMA; AR;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：237 / 247
页数：10
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