Publications related to the GRACE Missions (no abstracts)

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COSMIC-2 RFI Prediction Model Based on CNN-BiLSTM-Attention for Interference Detection and Location

Song, Cheng-Long, Jin, Rui-Min, Han, Chao, Wang, Dan-Dan, Guo, Ya-Ping, Cui, Xiang, Wang, Xiao-Ni, Bai, Pei-Rui, and Zhen, Wei-Min, 2024. COSMIC-2 RFI Prediction Model Based on CNN-BiLSTM-Attention for Interference Detection and Location. Sensors, 24(23):7745, doi:10.3390/s24237745.

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@ARTICLE{2024Senso..24.7745S,
       author = {{Song}, Cheng-Long and {Jin}, Rui-Min and {Han}, Chao and {Wang}, Dan-Dan and {Guo}, Ya-Ping and {Cui}, Xiang and {Wang}, Xiao-Ni and {Bai}, Pei-Rui and {Zhen}, Wei-Min},
        title = "{COSMIC-2 RFI Prediction Model Based on CNN-BiLSTM-Attention for Interference Detection and Location}",
      journal = {Sensors},
     keywords = {GNSS radio frequency interference, COSMIC-2, GNSS RO, CNN-BiLSTM-Attention, deep learning},
         year = 2024,
        month = dec,
       volume = {24},
       number = {23},
          eid = {7745},
        pages = {7745},
     abstract = "{As the application of the Global Navigation Satellite System (GNSS)
        continues to expand, its stability and safety issues are
        receiving more and more attention, especially the interference
        problem. Interference reduces the signal reception quality of
        ground terminals and may even lead to the paralysis of GNSS
        function in severe cases. In recent years, Low Earth Orbit (LEO)
        satellites have been highly emphasized for their unique
        advantages in GNSS interference detection, and related
        commercial and academic activities have increased rapidly. In
        this context, based on the signal-to-noise ratio (SNR) and
        radio-frequency interference (RFI) measurements data from
        COSMIC-2 satellites, this paper explores a method of predicting
        RFI measurements using SNR correlation variations in different
        GNSS signal channels for application to the detection and
        localization of civil terrestrial GNSS interference signals.
        Research shows that the SNR in different GNSS signal channels
        shows a correlated change under the influence of RFI. To this
        end, a CNN-BiLSTM-Attention model combining a convolutional
        neural network (CNN), bi-directional long and short-term memory
        network (BiLSTM), and attention mechanism is proposed in this
        paper, and the model takes the multi-channel SNR time series of
        the GNSS as the input and outputs the maximum measured value of
        RFI in the multi-channels. The experimental results show that
        compared with the traditional band-pass filtering inter-
        correlation method and other deep learning models, the model in
        this paper has a root mean square error (RMSE), mean absolute
        error (MAE), and correlation coefficient (R$^{2}$) of 1.0185,
        1.8567, and 0.9693, respectively, in RFI prediction, which
        demonstrates a higher RFI detection accuracy and a wide range of
        rough localization capabilities, showing significant
        competitiveness. Since the correlation changes in the SNR can be
        processed to decouple the signal strength, this model is also
        suitable for future GNSS-RO missions (such as COSMIC-1, CHAMP,
        GRACE, and Spire) for which no RFI measurements have yet been
        made.}",
          doi = {10.3390/s24237745},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024Senso..24.7745S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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