Solving the latency problem in real-time GNSS precise point positioning using open source software

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesReal-time Precise Point Positioning (PPP) can provide the Global Navigation Satellites Systems (GNSS) users with the ability to determine their position accurately using only one GNSS receiver. The PPP solution does not rely on a base receiver or local GNSS network. However, for establishing a real-time PPP solution, the GNSS users are required to receive the Real-Time Service (RTS) message over the Network Transported of RTCM via Internet Protocol (NTRIP). The RTS message includes orbital, code biases, and clock corrections. The GNSS users receive those corrections produced by the analysis center with some latency, which degraded the quality of coordinates obtained through PPP. In this research, we investigate the Support Vector Machine (SVR) and RandomForest (RF) as machine learning tools to overcome the latency for clock corrections in the CLK11 and IGS03 products. A BREST International GNSS Services permanent station in France selected as a case study. BNC software implemented in real-time PPP for around three days. Our results showed that the RF method could solve the latency problem for both IGS03 and CLK11. While SVR performed better on the IGS03 than CLK11; thus, it did not solve the latency on CLK11. This research contributes to establishing a simulation of real-time GNSS user who can store and predict clock corrections accordingly to their current observed latency. The self-assessment of the reproducibility level of this study has a rank one out of the range scale from zero to three according to the criteria and classifications are done by (Nüst et al., 2018)

Towards Advancing the Earthquake Forecasting by Machine Learning of Satellite Data

Earthquakes have become one of the leading causes of death from natural hazards in the last fifty years. Continuous efforts have been made to understand the physical characteristics of earthquakes and the interaction between the physical hazards and the environments so that appropriate warnings may be generated before earthquakes strike. However, earthquake forecasting is not trivial at all. Reliable forecastings should include the analysis and the signals indicating the coming of a significant quake. Unfortunately, these signals are rarely evident before earthquakes occur, and therefore it is challenging to detect such precursors in seismic analysis. Among the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range. Nevertheless, early studies on pre-earthquake and remote-sensing anomalies are mostly oriented towards anomaly identification and analysis of a single physical parameter. Many analyses are based on singular events, which provide a lack of understanding of this complex natural phenomenon because usually, the earthquake signals are hidden in the environmental noise. The universality of such analysis still is not being demonstrated on a worldwide scale. In this paper, we investigate physical and dynamic changes of seismic data and thereby develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1371 earthquakes of magnitude six or above due to their impact on the environment. We have analyzed and compared our proposed framework against several states of the art machine learning methods using ten different infrared and hyperspectral measurements collected between 2006 and 2013. Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databases

Modeling of GNSS derived vertical total electron content via artificial neural networks : a case study in Brazil

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2018.Uma das principais fontes de erro no posicionamento baseado em Sistemas Globais de Navegação por Satélite (GNSS) para usuários de receptores de uma frequência é o atraso de propagação nos sinais GNSS ao atravessarem a ionosfera. Esse atraso, em uma aproximação de primeira ordem, é diretamente proporcional ao Conteúdo Total de Elétrons (TEC). Assim, estimar o TEC é uma tarefa bastante relevante para correção dos efeitos ionosféricos sobre a propagação dos sinais. Para corrigir os erros de distância devido à ionosfera, os usuários de receptores GNSS de uma única frequência necessitam de modelos que representem o TEC. Neste cenário, este trabalho propõe a utilização de Redes Neurais Artificiais (ANN) para estimar o TEC obtido a partir de medidas GNSS na região do Brasil. As investigações apresentadas neste trabalho iniciam o desenvolvimento de um modelo regional que possa ser usado para determinar o TEC vertical sobre as regiões Nordeste, Centro-Oeste e Sul do Brasil, visando futuras aplicações em estimação próxima a tempo real e em previsão de curto prazo. Neste trabalho são utilizados dados GNSS das redes GLONASS para pesquisa e desenvolvimento, e da Rede Brasileira de Monitoramento Contínuo dos Sistemas GNSS (RBMC). Os parâmetros de entrada da rede neural baseiam-se em fatores que influenciam os valores do TEC, incluindo localização geográfica do receptor GNSS, atividade geomagnética, variações sazonais e diurnas e atividade solar. O modelo de ANN proposto é utilizado para estimar os valores de GNSS TEC vertical em regiões desprovidas de receptores GNSS de duas bandas de frequência que possam ser utilizados para tal fim. Diferentes análises são realizadas, divididas em três estudos de caso. Estas análises incluem a avaliação de desempenho espacial, avaliação de diferentes estruturas ANN, habilidade de previsão em curto-prazo e comparação de desempenho em relação aos Mapas Ionosféricos Globais (Global Ionospheric Maps) fornecidos pelo Centro para Determinação de órbita na Europa (CODE) durante a tempestade geomagnética registrada nos dias 13 e 14 de Outubro de 2016. Os resultados obtidos a partir das análises conduzidas sugerem que os modelos de NN propostos fornecem bom desempenho espacial e apresentam-se como ferramentas promissoras para aplicações de previsão de TEC de curto-prazo.One of the main error sources on Global Navigation Satellite Systems (GNSS) positioning solutions for users of single frequency receivers is the propagation refraction of the GNSS signals as they pass through the ionosphere. The estimation of the Total Electron Content (TEC) is very important for the correction of ionosphere propagation effects on GNSS signals. In order to correct the ionospheric range errors, GNSS single-frequency users need to rely on TEC models. In this framework, the present investigates the use of Artificial Neural Network models (ANN) to estimate TEC derived from GNSS measurements in Brazil. More specific, the investigations start the development of a regional model that can be used to determine the vertical TEC (vTEC) over Northeast, Central-West and South regions of Brazil, aiming future applications on a near real-time frame estimations and short-term forecasting. This work uses GNSS data from the GLONASS network for research and development, and from the Brazilian Network for Continuous Monitoring of the GNSS (RBMC). The input parameters of the ANN models are based on features known to influence TEC values, including the geographic location of the GNSS receiver, geomagnetic activity, seasonal and diurnal variations, and solar activity. The proposed ANN model is used to estimate the GNSS TEC values at void locations, where no dual-frequency GNSS receiver that may be used as a source of data for GNSS TEC estimation is available. Different analyses are carried out divided into three case studies. These analyses include spatial performance evaluation, evaluation of different ANN structures, short-term forecasting ability and performance comparison against CODE (Center for Orbit Determination in Europe) Global Ionospheric Maps during the geomagnetic storm registered on 13th and 14th October 2016. The results obtained from the described analysis suggest that the proposed ANN models provides good spatial performance and presents to be a promising tool for short-term forecasting applications

The impact of visibility range and atmospheric turbulence on free space optical link performance in South Africa.

Doctoral Degree. University of KwaZulu-Natal, Durban.In the recent years, the development of 5G and Massive Internet of Things (MIoT) technologies are fast increasing regularly. The high demand for a back-up and complimentary link to the existing conventional transmission systems (such as RF technology) especially for the “last-mile” phenomenon has increased significantly. Therefore, this has brought about a persistent requirement for a better and free spectrum availability with a higher data transfer rate and larger bandwidth, such as Free Space Optics (FSO) technology using very high frequency (194 −545 ) transmission system. There is currently unavailable comprehensive information that would enable the design of FSO networks for various regions of South Africa based on the impact of certain weather parameters such as visibility range (mainly in terms of fog and haze) and atmospheric turbulence (in terms of Refractive Index Structure Parameter (RISP)) on FSO link performance. The components of the first part of this work include Visibility Range Distribution (VRD) modeling using suitable probability density function (PDF) models, and prediction of the expected optical attenuation due to scattering and its cumulative distribution and modeling. The VRD modelling performed in this work, proposed various location-based PDF models, and it was suggested that the Generalized Pareto distribution model best suited the distributions of visibility in all the cities. The result of this work showed that the optical attenuation due to scattering within the coastal and near-coastal areas could reach as high as 169 / or more, while in the non-coastal areas it varies between 34 / and 169 /, which suggests significant atmospheric effects on the FSO link, mostly during the winter period. The BER performance analysis was performed and suitable mitigating techniques (such as 4 × 4 MIMO with BPSK and L-PPM schemes) were suggested in this work. The general two-term exponential distribution model provided a good fit to the cumulative distribution of the atmospheric attenuation due to scattering for all the locations. In order to ascertain how atmospheric variables contribute or affect the visibility range, which in turn determines the level of attenuation due to scattering, a time series prediction of visibility using Artificial Neural Network (ANN) technique was investigated, where an average reliability of about 83 % was achieved for all the stations considered. This suggests that climatic parameters highly correlate to visibility when they are all combined together, and this gave significant predictions which will enable FSO officials to develop and maintain a strategic plan for the future years. The modules of the second part of this work encompass the determination of the Atmospheric Turbulence Level (ATL) for each of the locations in terms of RISP (2) and its equivalent scintillation index, and then the estimation of the optical attenuation due to scintillation. The cumulative distributions of the optical attenuation due to scintillation and its modeling were also carried out. This research work has been able to achieve the prediction of the ground turbulence strength (through the US-Army Research Laboratory (US-ARL) Model) in terms of RISP using climatic data. In an attempt to provide a more reliable study into the atmospheric turbulence strength within South Africa, this work explores the characteristic behavior of several meteorological variables and other thermodynamic properties such as inner and outer characteristic scales, Monin-Obhukov length, potential temperature gradient, bulk wind shear and so on. According to the predicted RISP from meteorological variables (such as temperature, relative humidity, pressure, wind speed, water vapour, and altitude), location-based and general attenuation due to scintillation models were developed for South Africa to estimate the optical attenuation. The attenuation due to scintillation results show that the summer and autumn seasons have higher ATL, where January, February and December have the highest mean RISP across all the locations under study. Also, the comparison of the monthly averages of the estimated attenuations revealed that at 850 nm more atmospheric turbulence with specific attenuations between 21.04 / and 24.45 / were observed in the coastal and near-coastal areas than in the non-coastal areas. The study proposes the two-term Sum of Sine distribution model for the cumulative distribution of the optical attenuation based on scintillation, which should be adopted for South Africa. The obtained results in this work for the contributions of scattering and turbulence to the optical link, and the design of the link budget will serve as the major criteria parameters to further compare the outcomes of these results with that of the available terrestrial FSO systems and other conventional transmission systems like RF systems