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Prediction of Differential Code Bias Based on LR-KF-LSTM Model
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Siming LIAO1, Junna SHANG1, Mingkun SU1
Journal of Geodesy and Geodynamics | 2025, 45(9) : 915 - 921
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Journal of Geodesy and Geodynamics | 2025, 45(9): 915-921
Prediction of Differential Code Bias Based on LR-KF-LSTM Model
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Siming LIAO1, Junna SHANG1, Mingkun SU1
Affiliations
  • 1 School of Communication Engineering, Hangzhou Dianzi University, 1158 Second-Baiyang Street, Hangzhou 310018, China

    • LIAO Siming, postgraduate, majors in satellite navigation and positioning, and differential code bias estimation, E-mail:

About Author:

LIAO Siming, postgraduate, majors in satellite navigation and positioning, and differential code bias estimation, E-mail:

Published: 2025-09-15 doi: 10.14075/j.jgg.2024.10.476
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The presence of differential code bias(DCB) can affect the accuracy of total electron content(TEC) calculations and pseudorange observations, thereby impacting navigation, positioning, timing, and meteorological research results.In order to accurately estimate DCB, this paper analyzes the temporal variations of GPS inter-frequency DCB data provided by the Chinese Academy of Sciences(CAS) from 2021 to 2022 and proposes an LR-KF-LSTM combined model for precise prediction and analysis of DCB. Experimental results indicate that the average absolute percentage error of this method is less than 1.9%, the average absolute error is less than 0.03 ns, and the root mean square error is less than 0.04 ns.Compared with the LSTM model, BP neural network model, and the DCB values from the CAS product, the combined model shows better prediction performance under different solar activity and geomagnetic conditions. This combined network model can effectively predict satellite DCB and also provides a reference for addressing the issue of single-day or multi-day missing data in the DCB data CAS products.

differential code bias(DCB)  /  long short-term memory(LSTM) neural network  /  Kalman filter(KF)  /  linear regression(LR)  /  navigation positioning
廖思明, 尚俊娜, 苏明坤. 基于LR-KF-LSTM模型的差分码偏差预测分析. 大地测量与地球动力学, 2025 , 45 (9) : 915 -921 . DOI: 10.14075/j.jgg.2024.10.476
Siming LIAO, Junna SHANG, Mingkun SU. Prediction of Differential Code Bias Based on LR-KF-LSTM Model[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (9) : 915 -921 . DOI: 10.14075/j.jgg.2024.10.476
Funding
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  • Scientific Research Project of the Education Department of Zhejiang Province(Y202455360)
Appendix
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Year 2025 volume 45 Issue 9
28
11
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Article Info
doi: 10.14075/j.jgg.2024.10.476
  • Receive Date：2024-10-20
  • Online Date：2026-10-29
  • Published：2025-09-15
Article Data
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History
  • Received：2024-10-20
Funding
Scientific Research Project of the Education Department of Zhejiang Province(Y202455360)
Affiliations
    1 School of Communication Engineering, Hangzhou Dianzi University, 1158 Second-Baiyang Street, Hangzhou 310018, China
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光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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