Prediction of DGPS corrections with neural networks
Jizhang Sang, Kurt Kubik, Li Zhang · 2002
Applies neural network modelling to predicting DGPS (Differential Global Positioning System) corrections. The paper first briefly introduces GPS and DGPS navigation principles and aircraft navigation performance requirements. Following a discussion of the temporal characteristics of the DGPS corrections, a technique for predicting the DGPS corrections based on diagonal recurrent neural network (DRNN) modelling is presented. Numerical examples show that the prediction accuracy is better than 1 m for 10 s prediction and 1.3 m for 30 s prediction, respectively, which can maintain the aircraft navigation at the required accuracy for a period of 30 s.