Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
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.
• from the NASA Astrophysics Data System • by the DOI System •
@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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