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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50058

Title: ARMA Neural Networks for Predicting DGPS Pseudorange Correction
Authors: Dah-Jing Jwo
Tai-Shen Lee
Ying-Wei Tseng
Contributors: 國立臺灣海洋大學:通訊與導航工程學系
Keywords: ARMA
Neural Network
Signal Prediction
Date: 2004-05
Issue Date: 2018-09-14T01:32:49Z
Publisher: The Journal of Navigation
Abstract: Abstract: In this paper, the Auto-Regressive Moving-Averaging (ARMA) neural networks (NNs)
will be incorporated for predicting the differential Global Positioning System (DGPS)
pseudorange correction (PRC) information. The neural network is employed to realize the
time-varying ARMA implementation. Online training for real-time prediction of the PRC
enhances the continuity of service on the differential correction signals and therefore improves
the positioning accuracy. When the PRC signal is lost, the ARMA neural network
predicted PRC would temporarily provide correction data with very good accuracy. Simulation
is conducted for evaluating the ARMA NN based DGPS PRC prediction accuracy. A
comparative performance study based on two types of ARMA neural networks, i.e. Backpropagation
Neural Network (BPNN) and General Regression Neural Network (GRNN),
will be provided.
Relation: 57 pp.275–286
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50058
Appears in Collections:[通訊與導航工程學系] 期刊論文

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