Support Vector Regression Machine Learning Tool to Predict GNSS Clock Corrections in Real-Time PPP Technique

[EN] Real-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. GNSS users receive those corrections produced by the analysis center with some latency, which degraded the quality of coordinates obtained through realtime PPP. In this research, we investigate the Support Vector Machine (SVR) machine learning tool to overcome the latency for clock corrections in the IGS03 product. Three days of continuous GNSS observations at BREST permanent station in France were selected as a case study. BNC software was used to generate clock corrections files. Taking as reference the clock correction values without latency. The SVR solution shows a reduction in the standard deviation and range with about 30% and 20%, respectively, in comparison to the latency solution for all satellites except those satellites in GLONASS M block.Qafisheh, MWA.; Martín Furones, ÁE.; Torres-Sospedra, J. (2020). Support Vector Regression Machine Learning Tool to Predict GNSS Clock Corrections in Real-Time PPP Technique. CEUR Workshop Proceedings. 1-8. http://hdl.handle.net/10251/178545S1

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)

Metodología para el establecimiento de un sistema de alerta temprana GNSS para la monitorización de deformaciones en tiempo real

[EN] Early Warning System (EWS) for monitoring megastructures deformation, natural hazards, earthquakes, and landslides can prevent economic and life losses. Nowadays, Real-Time Precise Point Positioning (RT-PPP) plays a vital role in this domain since it relies on precise real-time measurements derived from a single receiver, provides real-time monitoring and global coverage. Nevertheless, RT-PPP measurements and methodology is very sensitive to outliers in products, latencies and changes in the constellation geometry. Consequently, there are long initialization periods, losses of convergence and different noise sources, with a high impact on the warning system's availability or even led out to initiate false warnings. This study presents the first experiment to propose a methodology that can help the decision-makers confirm the warning based on the probability of the detected movement by using machine learning classification models. For this, in the first experiment, a laser engraving machine device was modified to simulate deformations. A control unit will be designed based on open-source software, Python libraries are implemented, and the G programming language used to control the device motions. All this research will be the background on which the early warning service will be developed.[ES] Los sistemas de alerta temprana para la monitorización de deformaciones de estructuras, terremotos, movimientos de ladera u otro riego natural pueden prevenir pérdidas económicas y de vidas. El Posicionamiento Preciso de Punto en tiempo real (RT-PPP) ha demostrado ser útil en este escenario ya que se basa en medidas de precisión a partir de un único receptor, proporcionando cobertura global en tiempo real. A pesar de esto, la técnica RT-PPP es muy sensible a la precisión de los productos usados, latencia y cambios en la geometría de la constelación. Así, los periodos largos de inicialización de la técnica, la pérdida de convergencia de la solución o las diferentes fuentes de ruido, generan un gran impacto en la disponibilidad de un sistema de alerta temprana, pudiendo incluso generar falsas alarmas. Este trabajo presenta los primeros experimentos para generar un sistema de confirmación sobre una alerta temprana a partir de la probabilidad de detectar movimiento usado modelos de clasificación basados en técnicas de aprendizaje automático. Para esto, en un primer experimento, una máquina de grabado láser ha sido modificada para simular deformaciones. Se ha desarrollado una unidad de control basada en software libre, librerías de Python y el lenguaje de programación G que sirve para controlar los movimientos de la máquina. Este trabajo será la base sobre la que desarrollar, en un futuro, un servicio de alerta temprana.Qafisheh, M.; Martin, A.; Capilla, R. (2021). Proposed methodology for establishing an early GNSS warning system for real-time deformation monitoring. En Proceedings 3rd Congress in Geomatics Engineering. Editorial Universitat Politècnica de València. 54-60. https://doi.org/10.4995/CiGeo2021.2021.12691OCS546

A New Cooperative PPP-RTK System with Enhanced Reliability in Challenging Environments

Compared to the traditional PPP-RTK methods, cooperative PPP-RTK methods provide expandable service coverage and eliminate the need for a conventional expensive data processing center and the establishment and maintenance of a permanently deployed network of dense GNSS reference stations. However, current cooperative PPP-RTK methods suffer from some major limitations. First, they require a long initialization period before the augmentation service can be made available from the reference stations, which decreases their usability in practical applications. Second, the inter-reference station baseline ambiguity resolution (AR) and regional atmospheric model, as presented in current state-of-art PPP-RTK and network RTK (NRTK) methods, are not utilized to improve the accuracy and service coverage of the network augmentation. Third, the positioning performance of current PPP-RTK methods would be significantly degraded in challenging environments due to multipath effects, non-line-of-sight (NLOS) errors, poor satellite visibility and geometry caused by severe signal blockages. Finally, current position domain or ambiguity domain partial ambiguity resolution (PAR) methods suffer from high false alarm and miss detection, particularly in challenging environments with poor satellite geometry and observations contaminated by NLOS effect, gross errors, biases, and high observation noise. This thesis proposed a new cooperative PPP-RTK positioning system, which offers significant improvements to provide fast-initialization, scalable coverage, and decentralized real-time kinematic precise positioning with enhanced reliability in challenging environments. The system is composed of three major components. The first component is a new cooperative PPP-RTK framework in which a scalable chain of cooperative static or moving reference stations, generates single reference station-derived or reference station network-derived state-space-representation (SSR) corrections for fast ambiguity resolution at surrounding user stations with no need for a conventional expensive data processing center. The second component is a new multi-feature support vector machine (SVM) signal classifier based weight scheme for GNSS measurements to improve the kinematic GNSS positioning accuracy in urban environments. The weight scheme is based on the identification of important features in GNSS data in urban environments and intelligent classification of line-of-sight (LOS) and NLOS signals. The third component is a new PAR method based on machine learning, which employs the combination of two support vector machine (SVM) to effectively identify and exclude bias sources from PAR without relying on satellite geometry. The prototype of the new PPP-RTK system is developed and substantially tested using publically available real-time SSR products from International GNSS Service (IGS) Real-Time Service (RTS)

Tropospheric Correction for INSAR using Machine Learning

Interferometric Synthetic Aperture Radar (InSAR) is a popular technique for studying Earth\u27s surface deformation caused by phenomena like earthquakes and subsidence. However, its accuracy is limited by tropospheric delays caused by water vapor in the atmosphere. This limitation can be overcome by using methods that correct for tropospheric noise, such as statistical, empirical, and predictive approaches. This study explores the potential of using machine learning algorithms to predict the zenith total delay caused by tropospheric effects in InSAR measurements. The study employs two different machine learning algorithms, random forest and neural networks, to learn the relationship between numerical weather prediction model data and InSAR parameters in Continental USA and the globe. The neural network model outperforms both the random forest model and the traditional approach, reducing the RMSE by approximately 30%. The study demonstrates that machine learning algorithms can effectively correct tropospheric noise in most interferograms, resulting in a 30-60% improvement in Pennsylvania and Hawaii. However, the neural network model faces challenges in making predictions in areas with high variability in local climate and weather patterns. Overall, this research presents a promising approach for improving InSAR accuracy by using machine learning algorithms to correct for tropospheric noise --Abstract, p. ii

Innovative Solutions for Navigation and Mission Management of Unmanned Aircraft Systems

The last decades have witnessed a significant increase in Unmanned Aircraft Systems (UAS) of all shapes and sizes. UAS are finding many new applications in supporting several human activities, offering solutions to many dirty, dull, and dangerous missions, carried out by military and civilian users. However, limited access to the airspace is the principal barrier to the realization of the full potential that can be derived from UAS capabilities. The aim of this thesis is to support the safe integration of UAS operations, taking into account both the user's requirements and flight regulations. The main technical and operational issues, considered among the principal inhibitors to the integration and wide-spread acceptance of UAS, are identified and two solutions for safe UAS operations are proposed: A. Improving navigation performance of UAS by exploiting low-cost sensors. To enhance the performance of the low-cost and light-weight integrated navigation system based on Global Navigation Satellite System (GNSS) and Micro Electro-Mechanical Systems (MEMS) inertial sensors, an efficient calibration method for MEMS inertial sensors is required. Two solutions are proposed: 1) The innovative Thermal Compensated Zero Velocity Update (TCZUPT) filter, which embeds the compensation of thermal effect on bias in the filter itself and uses Back-Propagation Neural Networks to build the calibration function. Experimental results show that the TCZUPT filter is faster than the traditional ZUPT filter in mapping significant bias variations and presents better performance in the overall testing period. Moreover, no calibration pre-processing stage is required to keep measurement drift under control, improving the accuracy, reliability, and maintainability of the processing software; 2) A redundant configuration of consumer grade inertial sensors to obtain a self-calibration of typical inertial sensors biases. The result is a significant reduction of uncertainty in attitude determination. In conclusion, both methods improve dead-reckoning performance for handling intermittent GNSS coverage. B. Proposing novel solutions for mission management to support the Unmanned Traffic Management (UTM) system in monitoring and coordinating the operations of a large number of UAS. Two solutions are proposed: 1) A trajectory prediction tool for small UAS, based on Learning Vector Quantization (LVQ) Neural Networks. By exploiting flight data collected when the UAS executes a pre-assigned flight path, the tool is able to predict the time taken to fly generic trajectory elements. Moreover, being self-adaptive in constructing a mathematical model, LVQ Neural Networks allow creating different models for the different UAS types in several environmental conditions; 2) A software tool aimed at supporting standardized procedures for decision-making process to identify UAS/payload configurations suitable for any type of mission that can be authorized standing flight regulations. The proposed methods improve the management and safe operation of large-scale UAS missions, speeding up the flight authorization process by the UTM system and supporting the increasing level of autonomy in UAS operations

Analysis and Detection of Outliers in GNSS Measurements by Means of Machine Learning Algorithms

L'abstract è presente nell'allegato / the abstract is in the attachmen

Beyond 100: The Next Century in Geodesy

This open access book contains 30 peer-reviewed papers based on presentations at the 27th General Assembly of the International Union of Geodesy and Geophysics (IUGG). The meeting was held from July 8 to 18, 2019 in Montreal, Canada, with the theme being the celebration of the centennial of the establishment of the IUGG. The centennial was also a good opportunity to look forward to the next century, as reflected in the title of this volume. The papers in this volume represent a cross-section of present activity in geodesy, and highlight the future directions in the field as we begin the second century of the IUGG. During the meeting, the International Association of Geodesy (IAG) organized one Union Symposium, 6 IAG Symposia, 7 Joint Symposia with other associations, and 20 business meetings. In addition, IAG co-sponsored 8 Union Symposia and 15 Joint Symposia. In total, 3952 participants registered, 437 of them with IAG priority. In total, there were 234 symposia and 18 Workshops with 4580 presentations, of which 469 were in IAG-associated symposia. ; This volume will publish papers based on International Association of Geodesy (IAG) -related presentations made at the International Association of Geodesy at the 27th IUGG General Assembly, Montreal, July 2019. It will include papers associated with all of the IAG and joint symposia from the meeting, which span all aspects of modern geodesy, and linkages to earth and environmental sciences. It continues the long-running IAG Symposia Series

Improving Earthquake Monitoring and Early Warning Using GNSS Velocities and Machine Learning

Earthquake ground motions intersecting a population and seismically-triggered tsunamis have cost over 800,000 lives globally in the last 20 years. Distributed measurements of earthquake ground motions: 1) diagnose shaking intensity for rapid disaster response, 2) can alert to minimize damage ahead of the most destructive shaking and 3) are fundamental to understanding past events to inform future preparedness. Nearfield, conventional inertial seismic instruments saturate during the largest, most destructive earthquakes and may have limited regional availability. High-rate GNSS ground-station networks offer an alternative source of unsaturated ground motion measurements of medium to larger earthquakes. However, elevated noise levels of relative motion from space borne signals and minimal high-rate, larger magnitude event catalogs have limited the contribution of GNSS for current operational seismic monitoring, alerting and research. This thesis builds upon previous GNSS seismology research to address this gap between sensitivity and current functional range through a data-driven approach. A hemispheric network noise comparison determined time differenced carrier phase velocities is the geodetic processing method most sensitive to seismic signals. This method does not require external corrections and is more computationally efficient for our signal of interest at the highest rates and potentially on the network edge. This thesis then presents a supervised random forest classifier that outperformed existing detection methods when trained and tested on a catalog of high-rate GNSS velocity seismic waveforms to discriminate between signal and noise. This classifier can be run with minimal latency at high rates for robust stand-alone seismic-event detection. Lastly, zero-baseline inertial waveforms were augmented with stochastic GNSS noise time series to expand the GNSS seismic catalog. An expanded catalog improved generalization and will enable deeper learning. The analysis and models presented in this thesis lay a foundation for components of the next generation geodetic network.</p

Beyond 100: The Next Century in Geodesy

This open access book contains 30 peer-reviewed papers based on presentations at the 27th General Assembly of the International Union of Geodesy and Geophysics (IUGG). The meeting was held from July 8 to 18, 2019 in Montreal, Canada, with the theme being the celebration of the centennial of the establishment of the IUGG. The centennial was also a good opportunity to look forward to the next century, as reflected in the title of this volume. The papers in this volume represent a cross-section of present activity in geodesy, and highlight the future directions in the field as we begin the second century of the IUGG. During the meeting, the International Association of Geodesy (IAG) organized one Union Symposium, 6 IAG Symposia, 7 Joint Symposia with other associations, and 20 business meetings. In addition, IAG co-sponsored 8 Union Symposia and 15 Joint Symposia. In total, 3952 participants registered, 437 of them with IAG priority. In total, there were 234 symposia and 18 Workshops with 4580 presentations, of which 469 were in IAG-associated symposia. ; This volume will publish papers based on International Association of Geodesy (IAG) -related presentations made at the International Association of Geodesy at the 27th IUGG General Assembly, Montreal, July 2019. It will include papers associated with all of the IAG and joint symposia from the meeting, which span all aspects of modern geodesy, and linkages to earth and environmental sciences. It continues the long-running IAG Symposia Series