Song Cheng-Long, Jin Rui-Min, Han Chao, Wang Dan-Dan, Guo Ya-Ping, Cui Xiang, Wang Xiao-Ni, Bai Pei-Rui, Zhen Wei-Min
School of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
China Research Institute of Radiowave Propagation, Qingdao 266107, China.
Sensors (Basel). 2024 Dec 4;24(23):7745. doi: 10.3390/s24237745.
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) 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.
随着全球导航卫星系统（GNSS）应用的不断拓展，其稳定性和安全性问题日益受到关注，尤其是干扰问题。干扰会降低地面终端的信号接收质量，严重时甚至可能导致GNSS功能瘫痪。近年来，低地球轨道（LEO）卫星因其在GNSS干扰检测方面的独特优势而备受重视，相关商业和学术活动迅速增加。在此背景下，本文基于COSMIC - 2卫星的信噪比（SNR）和射频干扰（RFI）测量数据，探索一种利用不同GNSS信号通道中SNR相关性变化来预测RFI测量值的方法，以应用于民用地面GNSS干扰信号的检测与定位。研究表明，在RFI影响下，不同GNSS信号通道中的SNR呈现相关变化。为此，本文提出一种结合卷积神经网络（CNN）、双向长短期记忆网络（BiLSTM）和注意力机制的CNN - BiLSTM - Attention模型，该模型以GNSS的多通道SNR时间序列为输入，输出多通道中RFI的最大测量值。实验结果表明，与传统带通滤波互相关方法及其他深度学习模型相比，本文模型在RFI预测中的均方根误差（RMSE）、平均绝对误差（MAE）和相关系数（R）分别为1.0185、1.8567和0.9693，具有更高的RFI检测精度和广泛的粗略定位能力，展现出显著的竞争力。由于可以处理SNR中的相关性变化以解耦信号强度，该模型也适用于未来尚未进行RFI测量的GNSS掩星任务（如COSMIC - 1、CHAMP、GRACE和Spire）。
