Publications related to the GRACE Missions (no abstracts)

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Improving LEO short-term orbit prediction using LSTM neural network

Zhang, Wei, Zhang, Keke, Li, Xingxing, and Huang, Jiande, 2025. Improving LEO short-term orbit prediction using LSTM neural network. Advances in Space Research, 76(1):481–496, doi:10.1016/j.asr.2025.04.067.

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@ARTICLE{2025AdSpR..76..481Z,
       author = {{Zhang}, Wei and {Zhang}, Keke and {Li}, Xingxing and {Huang}, Jiande},
        title = "{Improving LEO short-term orbit prediction using LSTM neural network}",
      journal = {Advances in Space Research},
     keywords = {Low earth orbit (LEO) satellite, Short-term orbit prediction, Long short-term memory (LSTM) neural network, Machine learning algorithm},
         year = 2025,
        month = jul,
       volume = {76},
       number = {1},
        pages = {481-496},
     abstract = "{Orbit prediction of low earth orbit (LEO) satellites is of paramount
        importance for LEO-augmented navigation. Currently, the most
        widely used approach for satellite orbit prediction in
        navigation domain is the dynamical propagation method, which
        necessitates a good understanding of orbital dynamics. However,
        this method is plagued by the rapid error accumulation as
        prediction time increases due to our limited knowledge of the
        complex orbit dynamics. An effective solution to this challenge
        is employing the machine learning algorithm, which is data-
        driven and requires no explicit physical knowledge, in orbit
        prediction of LEO satellites. We focus on improving LEO short-
        term (less than 120 min) orbit prediction using the long short-
        term memory (LSTM) neural network. To this end, we have
        constructed datasets of the entire year 2019 from seven LEO
        satellites to conduct the experiments. Historical orbit
        prediction errors, derived from the comparison between the
        dynamical-propagation-based predicted orbit and external precise
        orbit, along with multiple satellite status and environment
        features are trained to forecast future orbit prediction errors,
        which will subsequently serve as the compensation for improving
        the dynamical-propagation-based predicted orbit. Our findings
        reveal that the LSTM model can improve the accuracy of predicted
        orbit by more than 30 \% for most LEO satellites with a maximum
        percentage around 75 \%. Benefiting from the LSTM model, the
        prediction time for obtaining 5-cm accuracy of predicted orbit
        can be extended to (41.2, 42.0, 31.2, 37.9, 30.0, 86.3, 108.1)
        min for GRACE-C/D, Swarm-A/B/C, and Sentinel-3A/3B satellites,
        respectively. Additionally, generalization tests between
        different LEO satellites suggest that the LSTM model exhibits a
        commendable generalization ability when orbit prediction time is
        less than 30 min. As the prediction time increases, the model
        trained using datasets from one LEO satellite is more suitable
        for forecasting orbit prediction errors of multiple LEO
        satellites with comparable orbital altitude and orbital plane.}",
          doi = {10.1016/j.asr.2025.04.067},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025AdSpR..76..481Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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