A comparative study between performance of recurrent neural network and Kalman filter for DGPS corrections prediction

Recurrent neural networks (RNNs) trained by the real time recurrent learning algorithm are capable of storing sequential information from the past. This property makes these networks ideal for DGPS corrections prediction. In this paper, performance of RNN is compared with Kalman filter predictor (KP) based on a state dependant model for DGPS corrections prediction. The experimental tests results with real data are stated and discussed in this paper. The results on real data indicate that the KP can predict DGPS corrections with better accuracy, but more slow.