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 (R²) 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.

提出一种基于深度学习的电离层f₀F₂短期预报方法, 通过采用注意力机制的双向长短期记忆网络(Bidirectional Long Short-Term Memory Model With Attention Mechanism, BiLSTM-Attention)算法, 结合前7天垂测站电离层f₀F₂观测值、世界时、太阳活动指数及地磁活动指数作为输入, 实现了中国区域电离层f₀F₂的预报. 模型对比分析结果表明: 低纬度台站的预报误差显著高于中纬度台站, BiLSTM-Attention模型表现最优, 长短期记忆网络(LSTM)模型次之, 与国际参考电离层模型(IRI)相比, BiLSTM-Attention模型的均方根误差(RMSE)降低了44.2%, 平均绝对误差(MAE)降低47%, 而决定系数(R²)提升21.3%; 磁暴期间, BiLSTM-Attention模型成功捕捉中国区域电离层负暴效应(f₀F₂下降), 与观测值非常一致, 而IRI模型开启暴时模式后, f₀F₂预测值与实际观测值之间存在一定偏差; 随着预报时间从1 h增加至24 h, 模型预报误差呈系统性上升趋势, RMSE从0.99 MHz增至2.05 MHz, MAE从0.69 MHz升至1.57 MHz, R²则由0.93减至0.75. 相关研究为空间天气预警及短波通信系统优化提供了高精度电离层参数的预报支撑.