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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. [ABSTRACT FROM AUTHOR]

To realize the automatic detection of solar radio burst (SRB) intensity, detection based on a modified multifactor support vector machine (SVM) algorithm is proposed. First, the influence of SRB on global navigation satellite system (GNSS) signals is analyzed. Feature vectors, which can reflect the SRB intensity of stations, are also extracted. SRB intensity is classified according to the solar radio flux, and different class labels correspond to different SRB intensity types. The training samples are composed of feature vectors and their corresponding class labels. Second, training samples are input into SVM classifiers to one-against-one training to obtain the optimal classification models. Finally, the optimal classification model is synthesized into a modified multifactor SVM classifier, which is used to automatically detect the SRB intensity of new data. Experimental results indicate that for historical SRB events, the average accuracy of SRB intensity detection is greater than 90% when the solar incident angle is higher than 20°. Compared with other methods, the proposed method considers many factors with higher accuracy and does not rely on radio telescopes, thereby saving cost. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Taking advantage of the influence of solar radio bursts on the performance of global positioning system (GPS), a detection method of solar radio bursts based on the decrease of GPS carrier-to-noise ratio is proposed. Firstly, the solar incidence angle of the observation ground is calculated, then the "falling points" and "rising points" are selected to determine the time interval of the trough of a single satellite from the single observation ground, and finally the detection results of the solar radio bursts are determined by integrating multiple satellites and multiple observation grounds. The experimental results show that the detection rate of solar radio bursts increases with the increase of the solar incidence angle, the detection rate of solar radio bursts above 600 SFU on the L2 frequency band reaches more than 80%, and the detection effect on the L2 frequency band is better than that on L1. This detection method has a high recognition rate, low cost, does not depend on the radio telescope, and can carry out all-weather real-time monitoring. [ABSTRACT FROM AUTHOR]

The acquisition of signals is a precondition for tracking and solution calculation in software-based Global Navigation Satellite System (GNSS) receivers. The Parallel Code phase Acquisition (PCA) algorithm can simultaneously obtain the correlation results at every sampling point. However, if the number of sampling points that needs processing is large, this method will lead to a heavy computational load. Thus, we improve the process of the PCA algorithm and propose a novel algorithm that divides the signals into K (K is a constant) parts to achieve correlation and obtains the correlation results with a fusion algorithm. This algorithm can simultaneously obtain the correlation results for sampling points at an interval of K points. If the K value is selected appropriately, the computational load can be decreased by about 50%. Also, the Receiver Operating Characteristic (ROC) curves show that under a certain probability of false alarm, the detection probability of the proposed algorithms is 5% lower than that of the PCA algorithm. Therefore, the proposed algorithm can speed up the acquisition process with a slight decrease in detection probability. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

Intense solar radio bursts (SRBs) can increase the energy noise and positioning error of the bandwidth of global navigation satellite system (GNSS). The study of the interference from intense L-band SRBs is of great importance to the steady operation of GNSS receivers. Based on the fact that intense L-band SRBs lead to a decrease in the carrier-to-noise ratio ( C / N 0 ) of multiple GNSS satellites over a large area of the sunlit hemisphere, an intense L-band SRB detection method without the aid of a radio telescope is proposed. Firstly, the valley period of a single satellite at a single monitoring station is detected. Then, the detection of SRBs is achieved by calculating the intersection of multiple satellites and multiple stations. The experimental results indicate that the detection rates of GPS L2 and GLONASS G2 are better than those of GPS L1 L5, GLONASS G1, and Galileo E1 E5. The detection rate of SRBs can reach 80% with a flux density above 800 solar flux unit (SFU) at the L2 frequency of GPS. Overall, the detection rate is not affected by the satellite distribution relative to the Sun. The proposed detection method is low-cost and has a high detection rate and low false alarm rate. This method is a noteworthy reference for coping with interference in GNSS from intense L-band SRBs. [ABSTRACT FROM AUTHOR]