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Comparative Analysis of Time-Series Forecasting Models for eLoran Systems: Exploring the Effectiveness of Dynamic

Jianchen Di¹, Miao Wu¹, Jun Fu¹

¹School of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China.

Sensors (Basel, Switzerland)

|July 30, 2025

Summary

A new dynamic weighting (DW) model offers superior time-series forecasting for enhanced long-range navigation (eLoran) systems. This advanced method balances prediction accuracy with computational efficiency for real-time applications.

Keywords:

ASF prediction LSTM deep learning eLoran system random forest system performance optimisation

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Area of Science:

Navigation Systems
Machine Learning
Time-Series Analysis

Background:

Enhanced long-range navigation (eLoran) systems require accurate data prediction.
Existing forecasting models face challenges in balancing accuracy and computational efficiency.

Purpose of the Study:

To develop and evaluate an advanced time-series forecasting methodology for eLoran systems.
To compare the performance of various machine learning models, including LSTM, RF, and a novel DW model.

Main Methods:

Evaluation of five forecasting approaches: multivariate linear regression, LSTM, RF, LSTM-RF fusion, and DW.
Application of models to the ASF2 dataset for performance analysis.
Assessment of prediction accuracy and computational efficiency.

Main Results:

The dynamic weighting (DW) model demonstrated the highest prediction accuracy.
DW model achieved strong computational efficiency, outperforming LSTM and hybrid models.
The DW model dynamically adjusted contributions of LSTM and RF for optimal performance.

Conclusions:

The DW model provides a well-balanced solution for optimizing additional secondary phase factor (ASF) prediction in eLoran systems.
The DW model's combination of precision and operational efficiency makes it suitable for real-time applications.
This research highlights the broader applicability of real-time forecasting models in navigation technology.