Accuracy Improvement Model for Predicting Propagation Delay of Loran-C Signal Over a Long Distance
Yurong Pu, Xiaoyi Zheng, Dandan Wang, Xiaoli Xi
Abstract
We previously used back propagation neural network (BPNN) with the meteorological factors of the receiver point to establish a model for predicting propagation delay of Loran-C signal over a short distance. Nevertheless, for a long propagation path, it is not proper to use only the meteorological factors of the receiver point. In this letter, a propagation delay prediction model that considers multiweather and multipoint was established by using a more suitable generalized regression neural network (GRNN) over a long distance. We first compared three meteorological factors of six points on the propagation path, and found that they have obvious differences. Then, a propagation delay prediction model based on three meteorological factors of the six points is established with GRNN. Finally, the further comparison shows that on the one hand, GRNN is more suitable to establish the prediction model of propagation delay than BPNN; on the other hand, more points on the propagation path are considered, and the prediction accuracy of the model is higher.