Ionospheric scintillation frequently occurs in equatorial, auroral and polar regions, posing a threat to the performance of the global navigation satellite system (GNSS). Thus, the detection of ionospheric scintillation is of great significance in regard to improving GNSS performance, especially when severe ionospheric scintillation occurs. Normal algorithms exhibit insensitivity in strong scintillation detection in that the natural phenomenon of strong scintillation appears only occasionally, and such samples account for a small proportion of the data in datasets relative to those for weak/moderate scintillation events. Aiming at improving the detection accuracy, we proposed a strategy combining an improved eXtreme Gradient Boosting (XGBoost) algorithm by using the synthetic minority, oversampling technique and edited nearest neighbor (SMOTE-ENN) resampling technique for detecting events imbalanced with respect to weak, medium and strong ionospheric scintillation. It outperformed the decision tree and random forest by 12% when using imbalanced training and validation data, for tree depths ranging from 1 to 30. For different degrees of imbalance in the training datasets, the testing accuracy of the improved XGBoost was about 4% to 5% higher than that of the decision tree and random forest. Meanwhile, the testing results for the improved method showed significant increases in evaluation indicators, while the recall value for strong scintillation events was relatively stable, above 90%, and the corresponding F1 scores were over 92%. When testing on datasets with different degrees of imbalance, there was a distinct increase of about 10% to 20% in the recall value and 6% to 11% in the F1 score for strong scintillation events, with the testing accuracy ranging from 90.42% to 96.04%.

Prediction of ionospheric total electron content (TEC) enables accurate ionospheric delay correction for global navigation satellite system (GNSS) services. Thus, it is crucial to extract the characteristics of the temporal periodicity and spatial correlation for TEC modeling and prediction. The self-attention mechanism of the transformer structure is utilized to capture the long-term characteristics of the TEC in China. The gated recurrent unit (GRU) network and long short-term memory (LSTM) were chosen for comparison because of their capability for nonlinear time series modeling. The results indicate that the proposed model outperforms LSTM by 23% from 2016 to 2018 and GRU by more than 4% from 2016 to 2017. In terms of small, moderate, and intense geomagnetic storms, the relative error at all involved latitude levels was reduced by 40%, 39%, and 5%, respectively, compared with GRU, as well as 44%, 47%, and 12%, respectively, compared with LSTM. Furthermore, the ensemble model (Ense-Trans) based on two proposed spatially embedded transformer models (Trans), compared with the original Trans model, reduced the relative error by 11.38%, 8.78%, and 11.78% in the summer, autumn, and winter of 2018, respectively. It is concluded that Trans has much better performance than GRU and LSTM, and Ense-Trans is better than Trans itself in terms of TEC prediction. This study will be of great significance for ionospheric real-time research and other space weather applications in the future.

The Global Positioning System (GPS) plays an important role in navigation and positioning services. When GPS signals propagate through a complex space environment, they are susceptible to interference of ionospheric scintillation. As one of the biggest interference sources on GPS navigation and positioning, ionospheric scintillation will lead to signal intensity decline and carrier phase fluctuation, making signal acquisition of the GPS receiver challenging. Thus, an acquisition algorithm based on differential coherent integration combining accumulation correlation and bit sign transition estimation is proposed. The coherent accumulation is applied to reduce computational loads and contribution by the Gaussian white noise in the signal. Moreover, the differential coherence integration is utilized to eliminate data blocks with bit transition, prolonging the coherence integration time and improving the data utilization rate. Experimental results show that under severe ionospheric scintillation condition, weak GPS signals can be acquired successfully after improving the acquisition algorithm, with the acquisition probability reaching 50% when the signal-to-interference ratio (SIR) drops to −34 dB. Comparing to the differential coherence integration, the complexity of the calculation reduces to only 21.75% effectively after the improvement. The execution time is less than half of the differential coherence integral.

To address the complex spatiotemporal characteristics of ionospheric total electron content (TEC), a bidirectional convolutional long short‐term memory network with a dynamic cross‐attention mechanism for spatiotemporal feature weighting (CASF‐BiConvLSTM) is proposed and further combined with the sliding interquartile range (SIQR) method, referred to as CASF‐SIQR, for pre‐earthquake TEC anomaly detection. TEC data from the Center for Orbit Determination in Europe (CODE) are divided into training (1999–2014), validation (2015), and testing (2016–2018) sets. Compared with the BiConvLSTM baseline, the proposed CASF‐BiConvLSTM achieves consistent improvements across annual, seasonal, and latitudinal variations, with an average root mean square error reduction of about 21% during 2016–2018. For TEC anomaly detection, the CASF‐SIQR framework achieves significantly lower false rates during solar and geomagnetic quiet periods, underscoring the importance of accurate TEC prediction in effectively suppressing false anomalies. During the Jiuzhaigou and Luding earthquakes, more TEC anomalies were identified than with the traditional SIQR method, with detection rates improved by 22.23% and 13.29%, respectively. During the pre‐earthquake periods, the detection rates reach 40.83% and 32.78%, representing improvements of 23.61% and 13.73% over SIQR. During the strong geomagnetic storm on 26 August 2018, the validation reveals that the proposed framework can utilize the spatiotemporal characteristics of detected TEC anomalies to distinguish geomagnetic storm disturbances from pre‐earthquake anomalies. These results demonstrate the framework's greater sensitivity and effectiveness across geophysical conditions, offering valuable insights for TEC modeling, prediction, and detection of potential earthquake‐related anomalies.