Ionospheric scintillation often occurs in the polar and equator regions, and it can affect the signals of the Global Navigation Satellite System (GNSS). Therefore, the ionospheric scintillation detection applied to the polar and equator regions is of vital importance for improving the performance of satellite navigation. GNSS radio occultation is a remote sensing technique that primarily utilizes GNSS signals to study the Earth’s atmosphere, but its measurement results are susceptible to the effects of ionospheric scintillation. In this study, we propose an ionospheric scintillation detection algorithm based on the Sparrow-Search-Algorithm-optimized Extreme Gradient Boosting model (SSA-XGBoost), which uses power spectral densities of the raw signal intensities from GNSS occultation data as input features to train the algorithm model. To assess the performance of the proposed algorithm, we compare it with other machine learning algorithms such as XGBoost and a Support Vector Machine (SVM) using historical ionospheric scintillation data. The results show that the SSA-XGBoost method performs much better compared to the SVM and XGBoost models, with an overall accuracy of 97.8% in classifying scintillation events and a miss detection rate of only 12.9% for scintillation events with an unbalanced GNSS RO dataset. This paper can provide valuable insights for designing more robust GNSS receivers.

Satellite navigation signals are feeble when they reach the ground, so they are vulnerable to attacks from outside interference signals. By emitting spoofing interference signals similar to real satellite signals, spoofing interference can make receivers give wrong navigation, position, and time information, and it is challenging to detect. This seriously affects the safe use of GNSS; therefore, it is essential to identify spoofing interference signals quickly and accurately. In our study, we proposed a novel six-array spoofing-interference-monitoring array antenna, which achieved the detection and identification of spoofing interference sources by monitoring the relevant peaks and combining an airspace-trapping algorithm. Moreover, we quickly accomplished our search for the whole circumferential ambiguity by using long- and short-baseline algorithms, which can realize the high-precision detection of spoofing interference sources. To verify this method’s accuracy, we conducted outdoor real experiments using a special spoofing interference source, and our experimental results show that our proposed array antenna’s directional accuracy for spoofing interference signals is kept within 2°, showing high spoofing interference direction-finding capability.

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.

Global Navigation Satellite System (GNSS) spoofing induces the target receiver to obtain the wrong positioning and timing results, which is very harmful. It is necessary to develop high-precision GNSS spoofing detection and associated direction-finding methods. In order to achieve sensitive and high-precision direction-finding for GNSS spoofing, it is necessary to realize the spoofing signal detection in the capture phase. This paper first proposes a method of GNSS spoofing detection, based on machine learning, that extracts features in the capture phase, which realizes various types of spoofing detection such as matching power, carrier phase alignment, and frequency locking. Notably, existing spoofing-direction-finding methods are mainly based on dedicated antenna arrays, which incur high costs and are not conducive to large-scale deployments. The basis of the spoofing detection proposed by this paper consists of a differential phase-center correction method, which is proposed in the context of an off-the-shelf anti-jamming array antenna, which effectively reduces the impact of the phase-center jitter introduced by the mutual coupling between antenna arrays on the direction-finding. The publicly accessible Texas Spoofing Test Battery (TEXBAT) dataset and actual measured data are both used for test verification. The results demonstrate that the proposed spoofing detection method can achieve success rates of over 97% on the TEXBAT dataset and more than 96% on the measured dataset, and the accuracy of the proposed direction-finding method can reach 1°, which can realize the effective detection and direction-finding of GNSS spoofing.

Ionospheric scintillation represents a disturbance phenomenon induced by irregular electron density variations, predominantly occurring in equatorial, auroral, and polar regions, thereby posing significant threats to Global Navigation Satellite Systems (GNSS) performance. Polar regions in particular confront distinctive challenges, including the sparse deployment of dedicated ionospheric scintillation monitoring receiver (ISMR) equipment, the limited availability of strong scintillation samples, severely imbalanced training datasets, and the insufficient sensitivity of conventional Deep Neural Networks (DNNs) to intense scintillation events. To address these challenges, this study proposes a modeling framework that integrates residual neural networks (ResNet) with the Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN). The proposed model incorporates multi-source disturbance features to accurately estimate phase scintillation indices (${\sigma }_{\phi }$) in polar regions. The methodology was implemented and validated across multiple polar observation stations in Canada. Shapley Additive Explanations (SHAP) interpretability analysis reveals that the rate of total electron content index (ROTI) features contribute up to 64.09% of the predictive weight. The experimental results demonstrate a substantial performance enhancement compared with conventional DNN models, with root mean square error (RMSE) values ranging from 0.0078 to 0.038 for daytime samples in 2024, and an average coefficient of determination (R²) consistently exceeding 0.89. The coefficient of determination for the Pseudo-Random Noise (PRN) path estimation results can reach 0.91. The model has good estimation results at different latitudes and is able to accurately capture the distribution characteristics of the local strong scintillation structures and their evolution patterns.

Ionospheric scintillation and radio frequency interference (RFI) affect Global Navigation Satellite System (GNSS) signals, making their distinction important for ionospheric monitoring and interference detection. This study identifies GNSS signal anomalies associated with scintillation and RFI using COSMIC-2 1 Hz precise orbit determination (POD) observations. Using 60 s per-satellite windows, dual-frequency time-series inputs and 16-dimensional statistical features were extracted, and weak labels for Normal, Scintillation, and RFI were generated from the amplitude scintillation index S₄ and the RFI index. An InceptionTimeLite-FiLM-DeepSets joint multi-satellite model, where FiLM denotes feature-wise linear modulation, was trained and tested on 2024 data and directly applied to the full-year 2025 dataset. On the 2024 test set, recall for Normal, Scintillation, and RFI was 98.9%, 76.1%, and 58.5%, respectively, with a Macro-F1 of 0.8227 and a Matthews correlation coefficient of 0.7120. Predicted Scintillation occurrence rates were higher at low magnetic latitudes and during the postsunset premidnight period, whereas predicted RFI occurrence rates were concentrated over North Africa, the Middle East, South Asia, and Southeast Asia. These results show that, within the weak-label framework, COSMIC-2 1 Hz POD observations can support GNSS signal anomaly classification and spatiotemporal distribution analysis, providing a complementary approach for long-term anomaly analysis.