Appraisal of ANN and ANFIS for Predicting Vertical Total Electron Content (VTEC) in the Ionosphere for GPS Observations

Positional accuracy in the usage of GPS receiver is one of the major challenges in GPS observations. The propagation of the GPS signals are interfered by free electrons which are the massive particles in the ionosphere region and results in delays in the transmission of signals to the Earth. Therefore, the total electron content is a key parameter in mitigating ionospheric effects on GPS receivers. Many researchers have therefore proposed various models and methods for predicting the total electron content along the signal path. This paper focuses on the use of two different models for predicting the Vertical Total Electron Content (VTEC). Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) algorithms have been developed for the prediction of VTEC in the ionosphere.  The developed ANN and ANFIS model gave Root Mean Square Error (RMSE) of 1.953 and 1.190 respectively.  From the results it can be stated that the ANFIS is more suitable tool for the prediction of VTEC. Keywords: Artificial Neural Network, Adaptive Neuro Fuzzy Inference System, Vertical Total Electro

Exploiting new GNSS signals to monitor, model and mitigate the ionospheric effects in GNSS

Signals broadcast by the Global Navigation Satellite Systems (GNSS) enable global, autonomous, geo-spatial positioning exploited in the areas such as geodesy, surveying, transportation and agriculture. The propagation of these signals is affected as they propagate through the Earth's upper atmosphere, the ionosphere, due to the ionic and electronic structure of the ionosphere. The ionosphere, a highly dynamic and spatially and temporally variable medium, can be the largest error source in Global Navigation Satellite System (Klobuchar 1991) in the absence of the Selective Availability. Propagation effects due to the ionosphere lead to errors in the range measurements, impact on receiver signal tracking performance and influence the GNSS positioning solution. The range error can vary from 1 to 100m depending on time of day, season, receiver location, conditions of the earth's magnetic field and solar activity (Hofmann-Wellenhof et al. 2001). This thesis focuses on modelling, monitoring and mitigating the ionospheric effects in GNSS within the scope of GNSS modernization, which introduces new signals, satellites and constellations. The ionosphere and its effects on GNSS signals, impact of the ionospheric effects at the receiver end, predicted error bounds of these effects under different solar, geomagnetic and ionospheric conditions, how these effects can be modelled and monitored with current and new (possible with GNSS modernization) correction approaches, degradation in the GNSS positioning solution and mitigation techniques to counter such degradation are investigated in this thesis. Field recorded and simulated data are considered for studying the refractive and diffractive effects of the ionosphere on GNSS signals, signal tracking performance and position solution. Data from mid-to-high latitudes is investigated for the refractive effects, which are due to dispersive nature of the ionosphere. With the use of multi-frequency, multi-constellation receivers, modelling of the refractive effects is discussed through elimination and estimation of these effects on the basis of dual and triple frequency approaches, concentrating on the benefit of the new GNSS signals. Data from the low latitudes is considered for studying the diffractive effects of the ionosphere, scintillation in particular, in GNSS positioning, and possible mitigation techniques to counter them. Scintillation can have a considerable impact on the performance of GNSS positioning by, for instance, increasing the probability of losing phase lock with a signal and reducing the accuracy of pseudoranges and phase measurements. In this sense, the impact of scintillation on signal tracking performance and position solution is discussed, where a novel approach is proposed for assessing the variance of the signal tracking error during scintillation. The proposed approach also contributes to the work related with scintillation mitigation, as discussed in this thesis. The timeliness of this PhD due to the recent and increasingly active period of the next Solar Cycle (predicted to reach a peak around 2013) and to the ongoing GNSS modernization give this research an opportunity to enhance the ionospheric knowledge, expertise and data archive at NGI, which is rewarding not only for this PhD but also for future research in this area

The development of an ionospheric storm-time index for the South African region

This thesis presents the development of a regional ionospheric storm-time model which forms the foundation of an index to provide a quick view of the ionospheric storm effects over South African mid-latitude region. The model is based on the foF2 measurements from four South African ionosonde stations. The data coverage for the model development over Grahamstown (33.3◦S, 26.5◦E), Hermanus (34.42◦S, 19.22◦E), Louisvale (28.50◦S, 21.20◦E), and Madimbo (22.39◦S, 30.88◦E) is 1996-2016, 2009-2016, 2000-2016, and 2000-2016 respectively. Data from the Global Positioning System (GPS) and radio occultation (RO) technique were used during validation. As the measure of either positive or negative storm effect, the variation of the critical frequency of the F2 layer (foF2) from the monthly median values (denoted as _foF2) is modeled. The modeling of _foF2 is based on only storm time data with the criteria of Dst 6 -50 nT and Kp > 4. The modeling methods used in the study were artificial neural network (ANN), linear regression (LR) and polynomial functions. The approach taken was to first test the modeling techniques on a single station before expanding the study to cover the regional aspect. The single station modeling was developed based on ionosonde data over Grahamstown. The inputs for the model which related to seasonal variation, diurnal variation, geomagnetic activity and solar activity were considered. For the geomagnetic activity, three indices namely; the symmetric disturbance in the horizontal component of the Earth’s magnetic field (SYM − H), the Auroral Electrojet (AE) index and local geomagnetic index A, were included as inputs. The performance of a single station model revealed that, of the three geomagnetic indices, SYM − H index has the largest contribution of 41% and 54% based on ANN and LR techniques respectively. The average correlation coefficients (R) for both ANN and LR models was 0.8, when validated during the selected storms falling within the period of model development. When validated using storms that fall outside the period of model development, the model gave R values of 0.6 and 0.5 for ANN and LR respectively. In addition, the GPS total electron content (TEC) derived measurements were used to estimate foF2 data. This is because there are more GPS receivers than ionosonde locations and the utilisation of this data increases the spatial coverage of the regional model. The estimation of foF2 from GPS TEC was done at GPS-ionosonde co-locations using polynomial functions. The average R values of 0.69 and 0.65 were obtained between actual and derived _foF2 over the co-locations and other GPS stations respectively. Validation of GPS TEC derived foF2 with RO data over regions out of ionospheric pierce points coverage with respect to ionosonde locations gave R greater than 0.9 for the selected storm period of 4-8 August 2011. The regional storm-time model was then developed based on the ANN technique using the four South African ionosonde stations. The maximum and minimum R values of 0.6 and 0.5 were obtained over ionosonde and GPS locations respectively. This model forms the basis towards the regional ionospheric storm-time index.Thesis (PhD) -- Faculty of Science, Physics and Electronics, 202

Exploiting new GNSS signals to monitor, model and mitigate the ionospheric effects in GNSS

Signals broadcast by the Global Navigation Satellite Systems (GNSS) enable global, autonomous, geo-spatial positioning exploited in the areas such as geodesy, surveying, transportation and agriculture. The propagation of these signals is affected as they propagate through the Earth's upper atmosphere, the ionosphere, due to the ionic and electronic structure of the ionosphere. The ionosphere, a highly dynamic and spatially and temporally variable medium, can be the largest error source in Global Navigation Satellite System (Klobuchar 1991) in the absence of the Selective Availability. Propagation effects due to the ionosphere lead to errors in the range measurements, impact on receiver signal tracking performance and influence the GNSS positioning solution. The range error can vary from 1 to 100m depending on time of day, season, receiver location, conditions of the earth's magnetic field and solar activity (Hofmann-Wellenhof et al. 2001). This thesis focuses on modelling, monitoring and mitigating the ionospheric effects in GNSS within the scope of GNSS modernization, which introduces new signals, satellites and constellations. The ionosphere and its effects on GNSS signals, impact of the ionospheric effects at the receiver end, predicted error bounds of these effects under different solar, geomagnetic and ionospheric conditions, how these effects can be modelled and monitored with current and new (possible with GNSS modernization) correction approaches, degradation in the GNSS positioning solution and mitigation techniques to counter such degradation are investigated in this thesis. Field recorded and simulated data are considered for studying the refractive and diffractive effects of the ionosphere on GNSS signals, signal tracking performance and position solution. Data from mid-to-high latitudes is investigated for the refractive effects, which are due to dispersive nature of the ionosphere. With the use of multi-frequency, multi-constellation receivers, modelling of the refractive effects is discussed through elimination and estimation of these effects on the basis of dual and triple frequency approaches, concentrating on the benefit of the new GNSS signals. Data from the low latitudes is considered for studying the diffractive effects of the ionosphere, scintillation in particular, in GNSS positioning, and possible mitigation techniques to counter them. Scintillation can have a considerable impact on the performance of GNSS positioning by, for instance, increasing the probability of losing phase lock with a signal and reducing the accuracy of pseudoranges and phase measurements. In this sense, the impact of scintillation on signal tracking performance and position solution is discussed, where a novel approach is proposed for assessing the variance of the signal tracking error during scintillation. The proposed approach also contributes to the work related with scintillation mitigation, as discussed in this thesis. The timeliness of this PhD due to the recent and increasingly active period of the next Solar Cycle (predicted to reach a peak around 2013) and to the ongoing GNSS modernization give this research an opportunity to enhance the ionospheric knowledge, expertise and data archive at NGI, which is rewarding not only for this PhD but also for future research in this area

Ionosphere Monitoring with Remote Sensing

This book focuses on the characterization of the physical properties of the Earth’s ionosphere, contributing to unveiling the nature of several processes responsible for a plethora of space weather-related phenomena taking place in a wide range of spatial and temporal scales. This is made possible by the exploitation of a huge amount of high-quality data derived from both remote sensing and in situ facilities such as ionosondes, radars, satellites and Global Navigation Satellite Systems receivers

Geodetic Sciences

Space geodetic techniques, e.g., global navigation satellite systems (GNSS), Very Long Baseline Interferometry (VLBI), satellite gravimetry and altimetry, and GNSS Reflectometry & Radio Occultation, are capable of measuring small changes of the Earth�s shape, rotation, and gravity field, as well as mass changes in the Earth system with an unprecedented accuracy. This book is devoted to presenting recent results and development in space geodetic techniques and sciences, including GNSS, VLBI, gravimetry, geoid, geodetic atmosphere, geodetic geophysics and geodetic mass transport associated with the ocean, hydrology, cryosphere and solid-Earth. This book provides a good reference for geodetic techniques, engineers, scientists as well as user community

Analysis of GNSS raw observations in PPP solutions

Global navigation satellite systems (GNSS) are an essential component in many areas of our daily life. They find application in diverse fields of private, commercial and scientific activities and are employed to meet the needs of police and military. Their fundamental importance for industrial countries is not the least the triggering point for the continuing modernisation of the existing and the development of new systems. The global satellite navigation systems are supplemented by regional satellite navigation systems (RNSS) and satellite based augmentation systems (SBAS). The diversity of systems, applied signal modulations and carrier frequencies, in particular in their combination, provide a broad range of opportunities along with new challenges. The work presented herein focuses on the use of satellite navigation systems for precise positioning and timing applications and scientific analysis. For best and comprehensive results, an equivalent combination of all available systems and signals is a fundamental requirement. For these reasons, relative approaches based on observation differences are rather inappropriate. Hence, this thesis focuses primarily on the method of precise point positioning (PPP) by waiving linear combinations. The objective is the development of a universal PPP analysis approach for standalone PPP and network solutions. Raw observations conserve the physical properties of original observation. This allows a detailed analysis of individual signal characteristics, but leads to the necessity of handling them. The utilisation of raw observations comes along with maximum flexibility. It allows for the application of physical error models as well as individual weighting and edition of all individual observation types. The possibility of a joint processing of all observations and the estimation of all parameters in a single run results in a significant simplification of the processing procedure. The first part of the thesis provides a general introduction to conventional GNSS analysis and highlights the limitations thereof. The second part introduces the technique of raw observations processing. It highlights the differences from the common ionosphere free processing approach and discusses the challenges. The concept presented for the analysis of GNSS raw observations is flexible and adjustable to any kind of GNSS application. This flexibility is attributed to a variety of different possible interpretations of the raw observation equation. In the frame of this thesis, a selection of different interpretations is introduced and demonstrated. One of the most important parameters for the analysis of raw observations is the so-called uncalibrated signal delays. The work presented exemplarily demonstrates their characteristics and discusses their implications for the analysis. For maximum stability of the results, it is common practice to resolve and apply integer carrier phase ambiguities. The presented work discusses and demonstrates the feasibility of this methodology for the implemented approach. It shows that the new approach simplifies the resolution of inter-GNSS carrier phase ambiguities and extends the spectrum of resolvable ambiguities. It is demonstrated that the proposed concept provides an “at least” equivalent alternative to the common processing strategies, applicable for highly precise standalone, as well as network PPP solutions, allowing for the simplified, consistent processing of different numbers of observation, suitable for an optimal, flexible, equivalent, joint processing of arbitrary GNSS observation types. It introduces a new dimension of analysis, with direct access to all individual observations and parameters

Imaging ionospheric irregularities by earth observation radar satellite

The sensitivity of Synthetic Aperture Radar (SAR) satellite signal in the L-band to ionospheric plasma density is used to obtain two-dimensional imaging of ionospheric density irregularities. As an application for equatorial ionosphere, we have recently reported first simultaneous observation of equatorial plasma bubble by the ALOS-2/PALSAR-2 satellite and a ground 630-nm airglow imager in northern Brazil. In this case, SAR ionospheric scintillation are represented as stripe-like signature of radar image over the terrain along the local magnetic field lines near an airglow depletion region. This so-called SAR scintillation stripes are discussed to be the signature of existing small-scale plasma irregularities with the scale size of hundreds of meters associated with equatorial plasma bubbles. We present the observational setup and the interpretation of SAR signal parameters to characterize the two-dimensional ionospheric density structures, and discuss future studies

Über die GPS-basierte Bestimmung troposphärischer Laufzeitverzögerungen

One major problem of precise GPS data analysis is that of modeling wetdelays with high precision. All conventional models have to fail in this task due to the impossibility of modeling wet delays solely from surface measurements like temperature and relative humidity. Actually, the non-hydrostatic component of the tropospheric propagation delay is highly influenced by the distribution of water vapor in the lower troposphere which cannot be sufficiently predicted with sole help of surface measurements. A work-around is to include atmospheric parameters as additional unknowns in the analysis of GPS data from permanent monitor stations that turns out to improve the quality of position estimates. Moreover, knowledge of zenith wet delays allows to obtain a highly interesting value for climatology and meteorology: integrated or precipitable water vapor being important for the energy balance of the atmosphere and holds share of more than 60% of the natural greenhouse effect. GPS can thereby contribute to the improvement of climate models and weather forecasting. This work outlines the application of ground-based GPS to climate research and meteorology without omitting the fact that precise GPS positioning can also highly benefit from using numerical weather models for tropospheric delay determination for applications where GPS troposphere estimation is not possible, for example kinematic and rapid static surveys. In this sense, the technique of GPS-derived tropospheric delays is seen as mutually improving both disciplines, precise positioning as well as meteorology and climatology