Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

ELoran Propagation Delay Prediction Model Based on BP Neural Network for Complex Meteorological Environment

Version 1 : Received: 26 April 2023 / Approved: 27 April 2023 / Online: 27 April 2023 (05:52:32 CEST)

A peer-reviewed article of this Preprint also exists.

Liu, S.; Guo, W.; Hua, Y.; Kou, W. ELoran Propagation Delay Prediction Model Based on a BP Neural Network for a Complex Meteorological Environment. Sensors 2023, 23, 5176. Liu, S.; Guo, W.; Hua, Y.; Kou, W. ELoran Propagation Delay Prediction Model Based on a BP Neural Network for a Complex Meteorological Environment. Sensors 2023, 23, 5176.

Abstract

The core of eLoran ground-based timing navigation system is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on Back-Propagation neural network (BPNN) for complex meteorological environment, which realizes the function of directly mapping propaga-tion delay fluctuation through meteorological factors. Firstly, the theoretical influence of meteoro-logical factors on each component of propagation delay is analyzed based on calculation parame-ters. Then, through the correlation analysis of the measured data, the complex relationship be-tween the seven main meteorological factors and the propagation delay, as well as their regional differences are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with the existing linear model and simple neural network model. eLoran; meteorological factor; propagation delay prediction model; Back-Propagation neural network;

Keywords

eLoran; meteorological factor; propagation delay prediction model; Back-Propagation neural network

Subject

Physical Sciences, Mathematical Physics

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