Satellite-based radio communication and navigation systems rely mostly on transionospheric propagation of radio signals concerning changes by total electron content (TEC). Therefore, it is necessary to understand the changing behavior of structural variations of ionospheric TEC for high-frequency applications besides mitigating the risks associated with the space weather impacts for navigation and aviation applications. The reference thresholds identified for moderate and severe levels of threshold activity for TEC by the International Civil Aviation Organization (ICAO) are 125 and 175 TECU, respectively. In view of this, statistical analysis of long-term ground-based global navigation satellite system (GNSS) TEC data of 25 years (1997–2021) using extreme value theory (EVT) is performed for both magnetically quiet and disturbed day conditions for four different regions – India (5-45°N), Japan low (20-30°N), Japan mid-latitudes (30–50°N), and global regions to identify the extreme ionospheric TEC events that take place once in 11, 22, 44, 66, 88, and 110 years with 95% confidence intervals. In the present work, both generalized extreme value (GEV) using annual maxima and generalized Pareto distribution (GPD) using peak-over-threshold (PoT) analysis are performed. A likelihood test and PoT constraint in addition to adjustment of shape parameter illustrate that GPD works better than GEV to identify return periods of extreme events. The recommended TEC moderate and severe thresholds for India, Japan low, Japan mid-latitude, and global regions are 121, 101, 90, and 139 TECU and 131, 135, 98, and 157 TECU, respectively. The analysis would be helpful in developing risk assessment and mitigation strategies for critical (GNSS) space weather systems.

Space weather adversely affects the satellite systems by obstructing the services of Global Positioning Systems (GPS), delaying the launch of space vehicles, and causing damage to basic infrastructures such as radio-communications and power distribution networks. The spatio-temporal variations of ionospheric Total Electron Content (TEC) during geomagnetic storms can be well examined by extreme value analysis. Statistical assessment and analysis of extreme values are necessary for developing risk assessment and mitigation strategies to the affected communication systems. In this study, extreme TEC events are investigated using 25 years (1997-2021) of long-term GEONET TEC data over Japan grid point location at 34.95°N and 134.05°E, and global data. In the present work, a generalized extreme value (GEV) distribution is applied to the annual maxima TEC to evaluate geomagnetic disturbances that could occur once in 44, 88, 132, and 176 years with a confidence interval of 95%. According to the reference thresholds indicated by International Civil Aviation Organization (ICAO) for safer aviation applications, the moderate and severe levels of threshold for TEC are identified as 125 and 175 TECU, respectively. The results illustrate that the estimated extreme TECs for one in 44, 88, 132, and 176 years are 119, 125, 128, 130 and 215, 220, 222, 225 TECU for grid point and global TEC data respectively. That is, a moderate threshold level of activity is likely to happen over considered grid point for a return period of beyond 88 years and severe threshold level of activity beyond 11 years for global TEC data.

Ionospheric scintillations caused by the ionospheric plasma density irregularities adversely affect the positional accuracy of the global navigation satellite system (GNSS) receiver. Machine learning methods are robust and efficient for detecting and classifying the ionospheric scintillation effects in GNSS signals. In this letter, we propose an extreme gradient boosting (XGBoost) based machine learning method to detect and classify ionospheric amplitude scintillation, applied on a large global positioning system (GPS) dataset collected from an equatorial ionization anomaly (EIA) region, Sao Jose, Brazil (geographic: 23.2°S 45.9°W; dip latitude: 20.9°S). The performance of the proposed method is compared with a classifier based on neural network (NN), support vector machine (SVM), decision tree (DT), and logistic regression (LR) methods. The confusion matrix results show that the XGBoost method performed well, with a prediction accuracy of 99.88% than other machine learning methods. XGBoost algorithm handles the data irregularities efficiently by setting a direction of descent through adaptive learning and can subsample between the columns to reduce the relevance of each weak learner. The performance results in terms of$F1$score, precision, recall, and area under precision-recall curve (AUC-PR) indicates that the XGBoost algorithm can characterize the ionospheric threats in GNSS signals to improve the position accuracy.

Equatorial electrojet (EEJ) and the subsequent development of equatorial ionization anomaly (EIA) are responsible for the highly complex and nonlinear variability nature of the ionosphere. Prediction of ionospheric parameters like Ionospheric F2 layer Critical frequency (foF2) and peak height (hmF2) feature at low latitude regions is of significant interest in understanding the ionospheric weather effects on communication and navigation systems. The role of artificial intelligence-based machine learning algorithms is successful in the prediction of ionospheric variability. In this letter, a deep learning model based on Bidirectional long short-term memory (Bi-LSTM) technique is implemented for predicting foF2 and hmF2 parameters. The Bi-LSTM method was trained and tested on one-year (2015) ionospheric foF2 and hmF2 data from Canadian Advanced Digital Ionosonde (CADI) located at Hyderabad, India (17.47 °N, 78.57 °E). Bi-LSTM model captures time sequence processing features using past and present foF2 and hmF2 data samples. It is evident from the results that the Bi-LSTM model performs better than long short-term memory (LSTM), neural networks (NNs), and International Reference Ionosphere (IRI) 2016 models in predicting foF2 and hmF2 values. The performance of the Bi-LSTM model tested and found to better predict ionospheric foF2 and hmF2 features for two significant geomagnetic storms occurred in the year 2015 (March and June).

Prominent advances in the field of artificial intelligence during the past decade and the breakthrough of deep learning would be useful for investigating ionospheric weather using ground and space-based ionospheric sensors data. The significance of deep learning algorithms needs to be assessed in forecasting the low latitude ionospheric disturbances (delays) for the global positioning system (GPS) signals. Total electron content (TEC) data sets prepared by taking advantage of GPS satellite radio frequency (RF) signals. This letter provides the application of deep learning models, long short-term memory (LSTM), gated recurrent unit (GRU), and a hybrid model that consists of LSTM combined with convolution neural network (CNN) to forecast the ionospheric delays for GPS signals. The deep learning models implemented using the vertical TEC (VTEC) time-series data estimated from GPS measurements over Bengaluru, Guntur, and Lucknow GPS stations. The LSTM-CNN model performs well when compared to other ionospheric deep learning forecasting algorithms with minimum root-mean-square error (RMSE) of 1.5 TEC units (TECUs) and a high degree of$R^{2} = 0.99$.

This letter proposes the implementation of ionospheric forecasting model based on the long short-term memory (LSTM) networks. Ionospheric region produces time delay for radio wave propagation of global positioning system (GPS) satellites. The ionospheric delays for GPS signals degrade the position accuracy in the measurements for precise navigation and positioning services. Utilizing the emerging artificial intelligence mathematical tools to forecast ionospheric disturbances using GPS-estimated total electron content (TEC) observations is decisive. In this letter, multi-input LSTM forecasting technique is investigated and tested for evaluating its capability in forecasting the ionospheric delays over Bengaluru station (16.26° N, 80.44° E) using eight years (2009-2016) of GPS measured vertical TEC (VTEC) time-series data. The assessment of the LSTM model performance during geomagnetic quiet and disturbed conditions is carried out in comparison with artificial neural networks model and International Reference Ionosphere (IRI-2016) model based on statistical parameters like root-mean-square error and coefficient of determination (R2). The experimental analysis delineates that the proposed LSTM model has provided the correlation of 0.99 with the GPS-measured VTEC and with a forecasting error of 1-2 TEC units.

In general, global positioning system (GPS) ranging errors and positioning caused by the ionosphere can be corrected by the Klobuchar ionospheric model. GPS satellites broadcast the model coefficients to the single-frequency users based on the average solar flux and seasonal variations. In low-latitude regions, such as India and Brazil, correction of the ionospheric delay based on these coefficients is not accurate because of the large gradients and complex dynamic ionospheric behavior. The traditional employment of refining the ionospheric Klobuchar model parameters with single-shell approximation is inappropriate for the equatorial/low-latitude regions. In this letter, we propose a technique to determine the ionospheric delay by using the new Klobuchar parameters (coefficients) based on multishell-spherical harmonics function (MS-SHF) analysis. It has been reported that by using the MS-SHF model, the ionospheric delays can be modeled accurately in the low-latitude regions. Furthermore, the proposed model performance has been evaluated with the Denis Bouvet (2017) single-frequency ionospheric correction model. In the single-frequency usage, the proposed model can improve (62.69%/77.08%) during quiet/disturbed days. Preliminary results reveal that the refined Klobuchar model parameters impart enhanced ionospheric delay corrections to regional navigation satellite systems with single-frequency GPS receivers, such as the Indian Regional Navigation Satellite System.

Plasma irregularities are a predominant feature over the low-latitude ionosphere within 20° north and south of the geomagnetic equator. The range delay errors introduced in the global navigation satellite system (GNSS) and in the space-based augmentation system by the plasma irregularities are difficult to measure due to the unpredictable nature of ionosphere. This letter presents a maximum-minimum eigen algorithm that has been developed for the efficient detection of ionospheric irregularities in the low latitudes. GNSS data were collected from five stations spread over the Indian terrain for the solar maximum year of 2013 and real-time detection of plasma irregularities was performed. The five GNSS stations, namely, Pbr2, Iisc, Guntur, Hyde, and Lck2 span over the geomagnetic latitudes ranging from 2.07° N to 17.92° N. The results show a very good correlation with the equatorial ionospheric disturbances. The occurrence of plasma irregularities was found to be a maximum at the ionospheric anomaly crest.

This letter addresses the problem of ionospheric scintillation effects on the global navigation satellite system (GNSS) signals. Severe scintillations degrade the signal intensity below the fade margin of the GNSS receiver, resulting in failure of the positioning and navigational services. A robust methodology is needed for the estimation and mitigation of such ionospheric scintillation effects. Hence, in this letter, the application of an adaptive signal decomposition technique based on variational mode decomposition (VMD), in combination with the detrended fluctuation analysis (DFA) method, is reported. VMD-DFA effectively decomposes the GNSS signal affected by ionospheric scintillations into a number of intrinsic mode functions and provides a threshold for the detection and mitigation of scintillations noise. Monte Carlo simulation results demonstrate that the proposed algorithm is superior and reliable for eliminating the amplitude scintillation effects compared to the complementary ensemble empirical mode decomposition method. The application of the proposed algorithm on both synthetic (Cornell scintillation model) and real-time measured GNSS data obtained from GNSS software navigation receiver at Rio de Janeiro, Brazil, has shown its potentiality in mitigating the ionospheric amplitude scintillation effects.

Use of Global Navigation Satellite Systems (GNSS) receivers for real-time applications has improved significantly all over the world. The main problem with the designed receivers is their failure to function under harsh environmental conditions because of the structured phase-locked loop (PLL) architecture. One of the most critical phenomena that cause signal degradation in GNSS receiver is ionospheric scintillations, which create disturbances in amplitude and phase of the received signal. The problem in signal acquisition and tracking, even in the severe canonical fades (deep amplitude fading correlated with reference to half cycle phase jumps), can be mitigated using robust and adaptive carrier tracking algorithms. The autoregressive exogenous modeling parameters are useful in estimating the amplitude and phase scintillations. The proposed adaptive-extended Kalman filter (AEKF) approach works as an effective carrier tracking algorithm maintaining a balance in dual problems faced by PLL-based receivers, i.e., (estimation versus mitigation) and (dynamics versus noise reduction) tradeoff. The developed AEKF algorithm performed well for synthetic Cornell scintillation monitor data and for Global Positioning System L1 PRN 12 data collected around 21.30 H (local time) on October 24, 2012, in Rio de Janeiro, Brazil, with GNSS Software Navigation Receiver.

A space-based augmentation system (SBAS) provides delay corrections to the Global Navigation Satellite System (GNSS) along with the residual error bounds falling within a high confidence interval. SBAS considerably improves the safety of an aircraft during flight even under all weather conditions. The dispersive nature of the ionosphere is the largest contributor of range error in GNSS, thus threatening its accuracy. Scrutiny of the ionospheric behavior over low latitudes is one of the most challenging tasks for any SBAS system. In this letter, an attempt has been made to detect the spatial gradients in the ionospheric vertical total electron content (VTEC), using statistical hypothesis tests, for the chosen probability of false alarm (Pfa) and probability of detection (Pd). Logarithm of likelihood ratio test (log LRT) and generalized likelihood ratio test (GLRT) were performed individually on GNSS data recorded by the multifrequency receiver at Koneru Lakshmaiah University, Guntur (GNT), Andhra Pradesh, India. The tests were performed on the data recorded on January 22, 2013, and March 17, 2013, which were geomagnetically quiet and disturbed days, respectively. The tests were validated by selectively introducing external noise in the VTEC and observing the outcomes. GLRT not only demonstrated a superior performance over log LRT, but it was also simpler to implement as it did not require any prior knowledge of the probability distribution of TEC values.

The Indian ionosphere is characterized by large variations, irregularities, and equatorial anomaly conditions. Therefore, an appropriate ionospheric model is necessary for satellite-based navigation systems. An ionospheric model based on spherical harmonic function (SHF) is used for modeling. Using the basis functions and measured vertical total electron content (TEC) data, spherical harmonic coefficients are computed by a weighted least square method. Seventeen stations of GPS Aided Geo Augmented Navigation TEC data are considered in the analysis. The results of the SHF model are presented in terms of accuracy of the residual error and compared with other standard grid models. The diurnal TEC variations are well depicted using the SHF model. The summer- and autumn-equinox-season ionospheric delay variations are also presented. The results indicate that the SHF model is capable of estimating ionospheric delays well.