The satellite clocks carried on the BeiDou navigation System (BDS) are a self-manufactured hydrogen clock and improved rubidium clock, and their on-orbit performance and stabilities are not as efficient as GPS and Galileo satellite clocks caused of the orbital diversity of the BDS and the complexity of the space operating environment. Therefore, the existing BDS clock product cannot guarantee the high accuracy demand for precise point positioning in real-time scenes while the communication link is interrupted. To deal with this problem, we proposed a deep learning-based approach for BDS short-term satellite clock offset modeling which utilizes the superiority of Long Short-Term Memory (LSTM) derived from Recurrent Neural Networks (RNN) in time series modeling, and we call it QPLSTM. The ultrarapid predicted clock products provided by IGS (IGU-P) and four widely used prediction methods (the linear polynomial, quadratic polynomial, gray system (GM (1,1)), and Autoregressive Integrated Moving Average (ARIMA) model) are selected to compare with the QPLSTM. The results show that the prediction residual is lower than clock products of IGU-P during 6-hour forecasting and the QPLSM shows a greater performance than the mentioned four models. The average prediction accuracy has improved by approximately 79.6, 69.2, 80.4, and 77.1% and 68.3, 52.7, 66.5, and 69.8% during a 30 min and 1-hour forecasting. Thus, the QPLSTM can be considered as a new approach to acquire high-precision satellite clock offset prediction.


    Access

    Download


    Export, share and cite



    Title :

    A Novel Long Short-Term Memory Predicted Algorithm for BDS Short-Term Satellite Clock Offsets


    Contributors:
    Tailai Wen (author) / Gang Ou (author) / Xiaomei Tang (author) / Pengyu Zhang (author) / Pengcheng Wang (author)


    Publication date :

    2021




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    Commercial Satellite Applications: Short Term and Long Term Trends

    Elbert, B. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 1998


    Aviation Safe Landing Prediction Utilizing Long-Short Term Memory Algorithm

    Krishna, Raji / Sariki, Tulasi Prasad / Sigamsetty, Siri Mahalakshmi et al. | IEEE | 2023



    Deep Learning Approach to Satellite Collision Avoidance Using Long Short-Term Memory

    Osama, Alaa / Raafat, Mourad / Abdelghafar, Sara et al. | Springer Verlag | 2024