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Mind evolutionary algorithm optimization in the prediction of satellite clock bias using the back propagation neural
Hongwei Bai1,2, Qianqian Cao3, Subang An4
1School of Environment and Spatial Informatics, China University Mining and Technology, Xuzhou, 221116, Jiangsu, China. 66173045@qq.com.
Scientific Reports
|February 6, 2023
Summary
This study introduces an optimized neural network model (MEA-BP) to improve satellite clock bias prediction accuracy for global navigation systems. The MEA-BP model demonstrates superior performance and stability compared to traditional methods.
Area of Science:
- * Satellite navigation and positioning systems
- * Computational intelligence and machine learning
- * Geodesy and geophysics
Background:
- * Satellite clock bias is a critical error source impacting Global Navigation Satellite System (GNSS) single-point positioning accuracy.
- * Traditional Backpropagation (BP) neural networks often encounter local optima, limiting their predictive capabilities.
- * Existing models like Grey Model (GM(1,1)) and standard BP networks have limitations in accurately predicting satellite clock bias.
Purpose of the Study:
- * To develop and evaluate a novel prediction model for satellite clock bias using an optimized BP neural network.
- * To enhance the initial weights and thresholds of the BP network through the Mind Evolutionary Algorithm (MEA).
- * To assess the model's accuracy, stability, and general applicability across various satellite types and data conditions.
Main Methods:
- * Implementation of a Mind Evolutionary Algorithm (MEA) to optimize the initial weights and thresholds of a Backpropagation (BP) neural network.
- * Application of one-time difference processing to satellite clock bias data for enhanced accuracy.
- * Comparative analysis of the MEA-BP model against the Grey Model (GM(1,1)) and standard BP neural network.
Main Results:
- * The MEA-BP model significantly improved prediction accuracy for satellite clock bias, with errors better than 0.74 ns (2h), 0.80 ns (3h), 1.12 ns (6h), and 0.87 ns (12h).
- * One-time difference processing notably enhanced the prediction accuracy and stability of all evaluated models.
- * The MEA-BP model exhibited greater robustness and less sensitivity to the length of the modeling sequence compared to the BP model, indicating strong anti-interference capabilities.
Conclusions:
- * The MEA-BP model offers a substantial improvement in satellite clock bias prediction accuracy and stability.
- * The proposed method demonstrates broad applicability across different satellite constellations and atomic clock types.
- * MEA-BP presents a more reliable and robust solution for mitigating clock bias errors in GNSS positioning.
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