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Global Navigation satellite systems (GNSS) are predominantly affected by Ionospheric space weather. The GNSS signal delays due to ionospheric Total Electron Content (TEC) can be forecasted using advances in the emerging mathematics tools and algorithms. Machine learning algorithms such as Gaussian Process Regression (GPR) is considered in the present paper to implement in the forecasting of low-latitude ionospheric conditions. The GPS receiver data is obtained for 8 years (2009–2016) during 24th solar cycle from International GNSS Services (IGS) station located in Bengaluru (Geographical latitude: 12.97° N, Geographical longitude: 77.59° E), India. The performance of GPR model is validated using statistical parameters such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and correlation coefficient. The results of the proposed GPR model were compared with the Auto Regressive Moving Average (ARMA) model and Artificial Neural Networks (ANN) model during solar maximum period and descending phase of 24th solar cycle. The experiment results are evident that GPR model is significantly providing the promising results in forecasting the ionospheric time delays for GNSS signals. The outcome of the work can be useful to develop web based Ionospheric TEC forecasting system to alert the GNSS users. • The GPS-TEC variability is investigated during solar maximum period and descending phase of 24th solar cycle. • The performance of ARMA, ANN and GPR models are evaluated in forecasting the ionospheric delays for GPS signals. • GPR model has better predicted TEC than the existing ARMA and ANN models during geomagnetic quiet and storm periods. • Kernel-based approach of GPR model is well suitable to forecast the ionospheric TEC variations over low latitudes. [ABSTRACT FROM AUTHOR]

Abstract Forecasting of ionospheric time delays has become a significant importance in satellite based navigation and communication system applications. Several researchers have been developed and implemented univariate Total Electron Content (TEC) forecasting models successfully over low, mid and high latitude regions. Therefore, identifying an effective multivariate forecasting technique is very essential to alert the Global Navigation Satellite System (GNSS) users under various space weather conditions. In this paper, a new multivariate ionospheric TEC forecasting model based on linear time series model in combination with Autoregressive and Moving Average (ARMA) is proposed and implemented using Bengaluru International GNSS Service (IGS) station data (geographic lat. −13.02°N, long. 77.57°E; geomagnetic latitude: 4.4°N) during the period of 8 years (2009–2016) in the 24th solar cycle. The major factors, namely, geomagnetic activity (Ap), solar Extreme Ultraviolet (EUV) irradiance (F 10.7 p), periodic oscillations (annual, semi-annual, terannual and biennial oscillations) and long-term trend are considered in the model as input parameters along with real time TEC observations. The proposed model is twofold: first, the impact of solar, geomagnetic, trend and periodic factors on TEC has been investigated from linear model. Second, ARMA method is applied for forecasting each factor. The forecasted individual factors are combined to obtain the forecasted TEC values. The estimated TEC from the proposed model has good agreement with the observed Global Positioning System (GPS) – TEC. It is noticed that the magnitudes of semi-annual variation have been reflected to be high during the High Solar Activity (HSA) period. It is also found that the geomagnetic effect on TEC is relatively low. The proposed multivariate ionospheric TEC forecasting model would be useful for characterizing the low-latitude ionospheric variations under various space weather conditions. [ABSTRACT FROM AUTHOR]

Abstract Cellular radio communication systems have become essential for data/voice/video/multimedia applications. The performance of cellular communication radio systems is assessed by considering the design specifications of frequency planning, channel assignment and interference mitigation strategies among others. Frequency planning is the most important component to improve capacity or quality of cellular radio systems. Large-scale path loss values between the base station and mobile stations are the key regulating factors that limit the performance of cellular systems, especially in urban/vegetation region. There is a necessity to develop a suitable path loss prediction model for predicting path loss values based on received signal strength measurements. In this paper, an ANN-based path loss model was used for macro cell measurement data obtained in the Vijayawada urban region, India. The Multi-Layer Perceptron (MLP) neural network model was considered. The prediction results indicate that the ANN model outperformed the Auto Regressive Moving Average (ARMA) and COST-231-WImodels. The outcome of this research work will be immensely useful for improving coverage and ensuring better frequency planning of cellular radio systems. [ABSTRACT FROM AUTHOR]

The Total Electron Content (TEC) is an essential component describing the temporal and spatial characteristics of the ionosphere. In this paper, an empirical orthogonal function (EOF) model is constructed by using ground based Global Navigational Satellite System (GNSS) TEC observation data at the Bangalore International GNSS Service (IGS) station (geographic – 13.02° N, 77.57° E; geomagnetic latitude 4.4° N) during an extended period (2009–2016) in the 24th solar cycle. EOF model can be decomposed into base functions and its corresponding coefficients. These decomposed modes well represented the influence of solar and geomagnetic activity towards TEC. The first three EOFs modes constitute about 98% of the total variance of the observed data sets. The Fourier Series Analysis (FSA) is carried out to characterize the solar-cycle, annual and semi-annual dependences by modulating the first three EOF coefficients with solar (F10.7) and geomagnetic (Ap and Dst) indices. The TEC model is validated during daytime and nighttime conditions as well as under different solar activity and geomagnetic conditions. A positive correlation (0.85) of averaged daily GPS-TEC with averaged daily F10.7 strongly supports those time-varying characteristics of the ionosphere features depends on the solar activity. Further, the validity and reliability of EOF model is verified by comparing with the GPS-TEC data, and standard global ionospheric models (International Reference Ionosphere, IRI2016 and Standard Plasmasphere-Ionosphere Model, SPIM). The performances of the standard ionospheric models are marked to be relatively better during High Solar Activity (HSA) periods as compared to the Low Solar Activity (LSA) periods. [ABSTRACT FROM AUTHOR]

Precise modeling of the ionospheric Total Electron Content (TEC) is a critical aspect of Positioning, Navigation, and Timing (PNT) services intended for the Global Navigation Satellite Systems (GNSS) applications as well as Earth Observation System (EOS), satellite communication, and space weather forecasting applications. In this paper, linear time series modeling has been carried out on ionospheric TEC at two different locations at Koneru Lakshmaiah University (KLU), Guntur (geographic 16.44° N, 80.62° E; geomagnetic 7.55° N) and Bangalore (geographic 12.97° N, 77.59° E; geomagnetic 4.53° N) at the northern low-latitude region, for the year 2013 in the 24th solar cycle. The impact of the solar and geomagnetic activity on periodic oscillations of TEC has been investigated. Results confirm that the correlation coefficient of the estimated TEC from the linear model TEC and the observed GPS-TEC is around 93%. Solar activity is the key component that influences ionospheric daily averaged TEC while periodic component reveals the seasonal dependency of TEC. Furthermore, it is observed that the influence of geomagnetic activity component on TEC is different at both the latitudes. The accuracy of the model has been assessed by comparing the International Reference Ionosphere (IRI) 2012 model TEC and TEC measurements. Moreover, the absence of winter anomaly is remarkable, as determined by the Root Mean Square Error (RMSE) between the linear model TEC and GPS-TEC. On the contrary, the IRI2012 model TEC evidently failed to predict the absence of winter anomaly in the Equatorial Ionization Anomaly (EIA) crest region. The outcome of this work will be useful for improving the ionospheric now-casting models under various geophysical conditions. [ABSTRACT FROM AUTHOR]

Global Positioning System (GPS) is a remote sensing tool of space weather and ionospheric variations. However, the interplanetary space-dependent drifts in the ionospheric irregularities cause predominant ranging errors in the GPS signals. The dynamic variability of the low-latitude ionosphere is an imperative threat to the satellite-based radio communication and navigation ranging systems. The study of temporal and spatial variations in the ionosphere has triggered new investigations in modelling, nowcasting and forecasting the ionospheric variations. Hence, in this paper, the dynamism in the day-to-day, month-to-month and seasonal variability of the ionospheric Total Electron Content (TEC) has been explored during the solar maximum period, January-December 2013, of the 24th solar cycle. The spatial and temporal variations of the ionosphere are analysed using the TEC values derived from three Indian low-latitude GPS stations, namely, Bengaluru, Guntur and Hyderabad, separated by 13–18° in latitude and 77–81° in longitude. The observed regional GPS-TEC variations are compared with the predicted TEC values of the International Reference Ionosphere (IRI-2012 and 2007) models. Ionospheric parameters such as Vertical TEC (VTEC), relative TEC deviation index and monthly variations in the grand-mean of ionosphere TEC and TEC intensity, along with the upper and lower quartiles, are adopted to investigate the ionosphere TEC variability during quiet and disturbed days. The maximum ionospheric TEC variability is found during March and September equinoxes, followed by December solstice while the minimum variability is observed during June solstice. IRI models are in reasonable agreement with GPS TEC but are overestimating during dawn hours (01:00–06:00 LT) as compared to the dusk hours. Higher percentage deviations are observed during equinoctial months than summer over EIA stations, Guntur and Hyderabad. GPS TEC variations are overestimated during dawn hours for all the seasons over Bengaluru. It has also been observed that positive storm effect (enhancement of TEC) is observed during the main phase of the March storm, 2013 (March 16–18, 2013) while both positive and negative storm effects (depletion of TEC) are registered during the main phase of the June storm, 2013 (June 28–30, 2013) at Bengaluru and Guntur, respectively. IRI-2012 model has slightly large discrepancies with the GPS-VTEC compared with the IRI-2007 model during the June storm, 2013 over Guntur station. This analysis highlights the importance of upgrading the IRI models due to their discrepancies during quiet and disturbed states of the ionosphere and developing an early warning forecast system to alert about ionosphere variability. [ABSTRACT FROM AUTHOR]

Ionospheric delay is one of the major atmospheric effects on the performance of satellite-based radio navigation systems. It limits the accuracy and availability of Global Positioning System (GPS) measurements, related to critical societal and safety applications. The temporal and spatial gradients of ionospheric total electron content (TEC) are driven by several unknown priori geophysical conditions and solar-terrestrial phenomena. Thereby, the prediction of ionospheric delay is challenging especially over Indian sub-continent. Therefore, an appropriate short/long-term ionospheric delay forecasting model is necessary. Hence, the intent of this paper is to forecast ionospheric delays by considering day to day, monthly and seasonal ionospheric TEC variations. GPS-TEC data (January 2013–December 2013) is extracted from a multi frequency GPS receiver established at K L University, Vaddeswaram, Guntur station (geographic: 16.37°N, 80.37°E; geomagnetic: 7.44°N, 153.75°E), India. An evaluation, in terms of forecasting capabilities, of three ionospheric time delay models – an Auto Regressive Moving Average (ARMA) model, Auto Regressive Integrated Moving Average (ARIMA) model, and a Holt-Winter's model is presented. The performances of these models are evaluated through error measurement analysis during both geomagnetic quiet and disturbed days. It is found that, ARMA model is effectively forecasting the ionospheric delay with an accuracy of 82–94%, which is 10% more superior to ARIMA and Holt-Winter’s models. Moreover, the modeled VTEC derived from International Reference Ionosphere, IRI (IRI-2012) model and new global TEC model, Neustrelitz TEC Model (NTCM-GL) have compared with forecasted VTEC values of ARMA, ARIMA and Holt-Winter’s models during geomagnetic quiet days. The forecast results are indicating that ARMA model would be useful to set up an early warning system for ionospheric disturbances at low latitude regions. [ABSTRACT FROM AUTHOR]

Investigation of ionospheric variability is essential for improving the daily ionospheric modeling and forecasting services of Global Navigation Satellite System (GNSS) applications. As India is a low-latitude region, more care has to be taken here to characterize the ionosphere due to irregularities and Equatorial Ionization Anomaly (EIA) conditions. Therefore, an appropriate method is required to diagnose the ionospheric variations during geomagnetic, solar and other disturbances. In this paper, the temporal ionospheric time delay variations were studied based on the Empirical Orthogonal Function (EOF) analysis and wavelet transforms (WT).These analyses were carried out with Total Electron Content (TEC) datasets obtained from three GNSS stations located in low-latitude regions. EOF analysis was performed on the TEC datasets, which were decomposed into a time series of orthogonal eigen values (or base functions) and associated coefficients. EOF base functions and their associated coefficients signify the hourly time variations and the day of the year variations. The results reveal that the first few EOFs represented the majority of TEC variability pertaining to the physical processes of the ionosphere. The accuracy of the EOF model was validated by the evaluation of observational TEC data with International Reference Ionosphere (IRI) 2012 models. The EOF model coefficients for each GNSS station showed a strong correlation with the IRI models and also described the correlation between the impacts of the level of geomagnetic activity on the ionosphere. The correlation coefficients for the first three EOFs were more than 0.95. The phase relationship of ionospheric TEC anomalies, with respect to the geomagnetic indices (Dst), were analyzed by wavelet transforms. [ABSTRACT FROM AUTHOR]

• The carrier phase measurements of NavIC are more precise that can provide better position accuracy. • Even after removing all the errors, the carrier phase measurements are biased by the receiver hardware. • Time differenced double difference technique is able compute the receiver noise in terms of differential phase bias. • The differential phase bias removed carrier phase measurements provide precise position. The pseudo-range (PR) and carrier phase (CP) observations of Global Navigation Satellite System (GNSS)/Navigation with Indian Constellation (NavIC) receivers are influenced due to various errors like ionosphere delay, troposphere delay, receiver and satellite clock error, multipath, integer ambiguity, receiver hardware noise etc. However, the real-time positioning and navigation applications are in quest of high-quality carrier phase measurements with zero bias. The GNSS/NavIC receiver hardware noise is one of the critical error sources that degrade the quality of carrier phase measurements. And it can be measured by computing the differential phase bias or carrier phase bias employing the double-difference method. This research article proposes a novel approach called Time Differenced Double Difference Carrier Phase (TDDDCP) for estimating differential phase bias or carrier phase bias of NavIC receiver. Moreover, the proposed TDDDCP technique has the advantage over the Double Difference Carrier Phase (DDCP) because it requires data from only one satellite with two zero base length receivers for which the differential phase bias is to be measured. The experimental results show that the differential phase bias value differs for different satellites, ranging from −0.3904 m to 0.3168 m. The bias-free carrier phase observations based on position coordinates (X, Y, Z) found improved precision of standard deviation of 0.4176 m, 0.645 m, and 0.328 m, respectively. [ABSTRACT FROM AUTHOR]

Given the continuous operation of satellite-based navigation applications, modeling of Total Electron Content (TEC) during magnetic disturbed periods is a significant challenge. The Global Positioning System (GPS)-TEC observations of the Bangalore station (13.02°N, 77.57°E) has been considered and covers the period (2009–2016) of the solar cycle 24. The study emphases on the analysis of TEC variations in eight geomagnetic storms of different intensity: (−223 nT < Dst < −80 nT). The QR decomposition is computed using the Gram-Schmidt (GS) process and is based on observational data from low latitude sectors. For interpolation, the QR model was evaluated on storms that occurred during different periods of solar activity (2009–2016), while for extrapolation the assessment was conducted for the intense storm of March 17, 2015 (St. Patrick's Day storm: Dst −223 nT) in different latitudes, covering the Asian sector between 10°N and 26°N. The R1 and Q1 modes patterns are consistent with changes in the solar proxy index (F10.7) and with regular daily variations and the correlation coefficient is 0.80 and 0.99. The post-residue between the QR model TEC and the GPS- TEC values is ±3 TECU. The QR model captured the TEC responses in consecutive storm cases (18–24, February 2014). The spatial variation of the TEC deviations increases as it moves towards the Equatorial Ionization Anomaly (EIA) crest from the magnetic equator and decreases beyond the crest. The proposed work could be useful for the further study of the Global Navigation Satellite System (GNSS) performance during geomagnetic magnetic disturbed periods. [ABSTRACT FROM AUTHOR]

Abstract: To investigate the equatorial ionospheric processes like the equatorial ionization anomaly (EIA), equatorial spread F (ESF) and their inter relationships, a network of ground based stations receiving the 150 and 400MHz transmissions from the low earth orbiting satellites (LEOS) is set up along the 77–78°E longitudes covering the equatorial and low-latitude region. The receivers at these stations measure the relative phase of the 150MHz beacon with respect to that at 400MHz, which is proportional to the slant total electron content (TEC) along the line of sight. These simultaneously measured TECs are used to obtain the latitude-altitude distribution of electron densities (tomograms) in the meridional plane. The tomograms are generated using the Algebraic reconstruction technique (ART), a standard inversion algorithm. In this paper, the first tomographic images of the equatorial ionosphere along the 77–78°E meridian are presented, signalling the addition of a new dimension to the investigation of equatorial ionosphere. The images indicate the temporal evolution of the equatorial ionosphere and EIA, as well as the variability in the strength of EIA at various local times. [Copyright &y& Elsevier]