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The ionospheric effect on radio wave propagation error has a significant impact on navigation, positioning, and real-time measurements. It is the primary source of errors in high precision aviation and measurement systems. A set of eight coefficients is used to correct ionospheric errors for Indian Regional Navigation Satellite System (IRNSS) users. Currently, the Klobuchar model is an effective low computational mathematical model for ionospheric range error mitigation through a set of eight coefficients. It has low accuracy in Equatorial Ionization Anomaly (EIA) regions and can correct up to ∼60% of the ionospheric range error. This paper proposes an ionospheric error correction model that can be applied to such measurements as well as an associated error model to compute accuracy with a differential interval of observations 5 m, 30 m and 60 m. The numerical simulation results show that compared to widely used algorithms, the proposed strategy can enhance the 83.13% ionospheric correction with Klobuchar- like coefficients at a sampling rate of 5 m. The proposed solution could be effective for Regional Navigational Satellite Systems (RNSS) for ionospheric delay corrections in low-latitude regions. [ABSTRACT FROM AUTHOR]

Abstract In the last 20 years, and in particular in the last decade, the availability of propagation data for GNSS has increased substantially. In this sense, the ionosphere has been sounded with a large number of receivers that provide an enormous amount of ionospheric data. Moreover, the maturity of the models has also been increased in the same period of time. As an example, IGS has ionospheric maps from GNSS data back to 1998, which would allow for the correlation of these data with other quantities relevant for the user and space weather (such as Solar Flux and Kp). These large datasets would account for almost half a billion points to be analyzed. With the advent and explosion of Big Data algorithms to analyze large databases and find correlations with different kinds of data, and the availability of open source code libraries (for example, the TensorFlow libraries from Google that are used in this paper), the possibility of merging these two worlds has been widely opened. In this paper, a proof of concept for a single frequency correction algorithm based in GNSS GIM vTEC and Fully Connected Neural Networks is provided. Different Neural Network architectures have been tested, including shallow (one hidden layer) and deep (up to five hidden layers) Neural Network models. The error in training data of such models ranges from 50% to 1% depending on the architecture used. Moreover, it is shown that by adjusting a Neural Network with data from 2005 to 2009 but tested with data from 2016 to 2017, Neural Network models could be suitable for the forecast of vTEC for single frequency users. The results indicate that this kind of model can be used in combination with the Galileo Signal-in-Space (SiS) NeQuick G parameters. This combination provides a broadcast model with equivalent performances to NeQuick G and better than GPS ICA for the years 2016 and 2017, showing a 3D position Root Mean Squared (RMS) error of approximately 2 m. [ABSTRACT FROM AUTHOR]

The HF propagation model, North Ionospheric Model and Ray Tracing (NIM-RT) was developed and tested for a number of years by comparing measured vertical and oblique ionograms over a number of radio links (especially in high latitude area) with the simulated ionograms. The present paper extends the model in order to include: (a) Implementation of the data retrieved from the International Reference Ionosphere (IRI-2012) model into the software for radio channel modeling. (b) The algorithm for IRI data optimization to the real time condition. (c) Results of comparison between simulated and measured ionograms. Based on these updates, a new software tool called North Ionospheric Model with IRI and Ray Tracing (NIMIRI-RT) was developed, and a number of vertical ionograms corresponding to multiple ionospheric reflections was simulated. The vertical ionograms observed at various ionosondes were compared with the synthesized ionograms, generated by applying NIM-RT in conjunction with initial and optimized IRI data. The ionogram structure simulated by NIMIRI-RT based on the data retrieved from optimized IRI is more reminiscent to the observations than ionograms synthesized with the initial NIMIRI-RT without parameters optimization. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer (foF2) one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years’ foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years’ data were used as a training dataset and the second eleven years’ data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32MHz, which is better than 0.34MHz from BPNN, 0.35MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78MHz, which is better than 0.81MHz from BPNN, 0.81MHz from SVR and 1.37MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper. [Copyright &y& Elsevier]

Vertical sounding is a widely used technique to obtain ionosphere measurements, such as an estimation of virtual height versus frequency scanning. It is performed by high frequency radar for geophysical applications called “ionospheric sounder” (or “ionosonde”). Radar detection depends mainly on targets characteristics. While several targets behavior and correspondent echo detection algorithms have been studied, a survey to address a suitable algorithm for ionospheric sounder has to be carried out. This paper is focused on automatic echo detection algorithms implemented in particular for an ionospheric sounder, target specific characteristics were studied as well. Adaptive threshold detection algorithms are proposed, compared to the current implemented algorithm, and tested using actual data obtained from the Advanced Ionospheric Sounder (AIS-INGV) at Rome Ionospheric Observatory. Different cases of study have been selected according typical ionospheric and detection conditions. [ABSTRACT FROM AUTHOR]

Abstract: Auroral nighttime infrared emission observed by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument onboard the Thermosphere–Ionosphere–Mesosphere Energetics and Dynamics (TIMED) satellite is used to develop an empirical model of geomagnetic storm enhancements to E-region peak electron densities. The empirical model is called STORM-E and will be incorporated into the 2012 release of the International Reference Ionosphere (IRI). The proxy for characterizing the E-region response to geomagnetic forcing is NO+(v) volume emission rates (VER) derived from the TIMED/SABER 4.3μm channel limb radiance measurements. The storm-time response of the NO+(v) 4.3μm VER is sensitive to auroral particle precipitation. A statistical database of storm-time to climatological quiet-time ratios of SABER-observed NO+(v) 4.3μm VER are fit to widely available geomagnetic indices using the theoretical framework of linear impulse-response theory. The STORM-E model provides a dynamic storm-time correction factor to adjust a known quiescent E-region electron density peak concentration for geomagnetic enhancements due to auroral particle precipitation. Part II of this series describes the explicit development of the empirical storm-time correction factor for E-region peak electron densities, and shows comparisons of E-region electron densities between STORM-E predictions and incoherent scatter radar measurements. In this paper, Part I of the series, the efficacy of using SABER-derived NO+(v) VER as a proxy for the E-region response to solar-geomagnetic disturbances is presented. Furthermore, a detailed description of the algorithms and methodologies used to derive NO+(v) VER from SABER 4.3μm limb emission measurements is given. Finally, an assessment of key uncertainties in retrieving NO+(v) VER is presented. [Copyright &y& Elsevier]

Abstract: The high frequency management system with backscatter radar supplies the real time ionosphere channel conditions to high frequency users, which leads to the demand for the ground range between the radar location and the scatters on the distant ground. The ionosphere electron density profile is usually inversed to obtain the ground range. An inversion algorithm, with which the ground range on the leading edge of the backscatter ionograms can be obtained without electron density, is presented in this paper. The ray path geometry of the backscatter sounding and the change in the group path on the leading edge with operating frequency are used to derive the ground range. Synthesized backscatter ionogram and experimental backscatter ionograms are processed to validate the algorithm. The results indicate that the algorithm is usable for high frequency management system. [Copyright &y& Elsevier]

Abstract: The Ionospheric Total Electron Content is responsible for the group delay of the signals from the Navigation satellites. This delay causes ranging error, which in turn degrades the accuracy of position estimated by the receivers. For critical applications, single frequency receivers resort to Satellite Based Augmentation Systems in order to have improved accuracy and integrity. The performance of these systems in terms of accuracy can be improved if predictions of the delays are available simultaneously with real measurements. This paper attempts to predict the Total Electron Content using adaptive recurrent Neural Network at three different locations of India. These locations are selected at the magnetic equator, at the equatorial anomaly crest and outside the anomaly range, respectively. In-situ Learning Algorithm has been used for tracking the non-stationary nature of the variation. Prediction is done for different prediction intervals. It is observed that, for each case, the mean and root mean square values of prediction errors remain small enough for all practical applications. Analysis of Variance is also done on the results. [Copyright &y& Elsevier]