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%.

The civil Global Positioning System (GPS) is vulnerable to spoofing because of its open signal structure. The performance of previous spoofing detection methods is often limited due to spoofing's strong concealment. In this study, a method is proposed to detect spoofing by analysing the features of improved signal quality monitoring (SQM) moving variance (MV), improved SQM moving average (MA), early‐late phase, carrier‐to‐noise ratio–MV and clock offset rate of receiver using Support Vector Machines. Then, the effectiveness of different kernel functions is compared along with other previous methods, revealing that our method outperforms previous methods when coarse Gaussian is used as kernel function. Specifically, the f1 score of the proposed method is improved by 3.22%, 12.85% and 35.72% in comparison with Back Propagation network, Ratio and Delta. The authors hope this work is beneficial for future research and for the implementation of GPS spoofing detection technology and high‐performance receiver, which is of great significance to maintain the normal operation of GPS.

The Global Navigation Satellite System (GNSS) becomes vulnerable in a challenging environment, among which spoofing is the most dangerous threat. Meaconing, as the most convenient way to conduct spoofing, is widely studied around the world, and also leads to lots of research into corresponding anti-spoofing techniques. This paper develops a semi-hardware meaconing platform and proposes a novel GPS meaconing spoofing detection method based on Improved Ratio combined with Carrier-to-noise Moving variance (C/N₀ − MV). The effectiveness has been validated theoretically and experimentally. The proposed method is proven useful when the meaconing signal has 5 dB power gain over the authentic signal, presenting 98% detection rate whereas the classic Signal quality monitoring (SQM) method with the Ratio metric presents only 30%.

The safety and efficiency of the merging zone is particularly important for traffic networks. Although autonomous vehicle improves the safety and efficiency from vehicle view, traffic controlling in merging zone mostly focus on improving efficiency from roadside view. Lacking of detailed driving recommendation, it ignores the safety of merging zone where commercial vehicle pose a high collision risk in real traffic. This paper proposes a roadside decision-making methodology to simultaneously improve the safety and efficiency of merging zone. We have built two modules, namely assessment and decision-making. Assessment module takes advantage of Bayesian inference to evaluate dynamic collision risk. Decision-making module based on deep reinforcement learning recommends the actions to commercial vehicles by roadside unit. A series of typical simulation tests show that our method increases the TTC of commercial vehicles by an average of 62.7%. In the free flow, the overall travel time of vehicles in the merging zone is reduced by 11.68%. Most notably, when congestion occurred, the average jam length is reduced by 59.68% on the premise of safety. Moreover, the average accuracy of the roadside decision-making method on the evaluation metrics of TTC, travel time, and jam length are 93.73%, 91.65%, and 94.45%, respectively. The experimental results show that the roadside decision-making methodology simultaneously improves safety and efficiency, and it dynamically adapts free and congested traffic flow.

Dynamic short-delay multipath in increasing dense urban canyons is a challenge to the global navigation satellite system (GNSS)-based positioning systems. However, the performance of existing multipath methods for a stand-alone receiver is limited on dealing with short-delay multipath. Vector tracking-based deep coupling with an inertial navigation system (INS) has the potential to suppress multipath errors by tracking direct signals using more accurate and robust positions. However, in the typical deep couplings reported, the unmodeled multipath error is generally regarded as noise with other errors, and consequently, their capacity for multipath suppression is not satisfying. To solve these problems, an improved deep coupling structure with a multipath error estimator (MPEE) is proposed and implemented in two schemes. Different from other methods, this modification creates nearly no extra complexity and computation cost. In this way, the inner correlation caused by multipath interference between channels can be decoupled, and the underlying requirements for modeling the errors accurately to construct vector feedback are fulfilled. Besides, for comparison analysis, a software-defined platform, including different tracking and integration approaches, is designed. Finally, the proposed methods are evaluated in a weak signal, short-delay multipath, dynamic multipath, and the real-life multipath scenarios, and their superiority in positioning, measurement improvement, and tracking robustness are validated.

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.