Spatial statistics and analysis of earth's ionosphere

Thesis (Ph.D.)--Boston UniversityThe ionosphere, a layer of Earths upper atmosphere characterized by energetic charged particles, serves as a natural plasma laboratory and supplies proxy diagnostics of space weather drivers in the magnetosphere and the solar wind. The ionosphere is a highly dynamic medium, and the spatial structure of observed features (such as auroral light emissions, charge density, temperature, etc.) is rich with information when analyzed in the context of fluid, electromagnetic, and chemical models. Obtaining measurements with higher spatial and temporal resolution is clearly advantageous. For instance, measurements obtained with a new electronically-steerable incoherent scatter radar (ISR) present a unique space-time perspective compared to those of a dish-based ISR. However, there are unique ambiguities for this modality which must be carefully considered. The ISR target is stochastic, and the fidelity of fitted parameters (ionospheric densities and temperatures) requires integrated sampling, creating a tradeoff between measurement uncertainty and spatio-temporal resolution. Spatial statistics formalizes the relationship between spatially dispersed observations and the underlying process(es) they represent. A spatial process is regarded as a random field with its distribution structured (e.g., through a correlation function) such that data, sampled over a spatial domain, support inference or prediction of the process. Quantification of uncertainty, an important component of scientific data analysis, is a core value of spatial statistics. This research applies the formalism of spatial statistics to the analysis of Earth's ionosphere using remote sensing diagnostics. In the first part, we consider the problem of volumetric imaging using phased-array ISR based on optimal spatial prediction ("kriging"). In the second part, we develop a technique for reconstructing two-dimensional ion flow fields from line-of-sight projections using Tikhonov regularization. In the third part, we adapt our spatial statistical approach to global ionospheric imaging using total electron content (TEC) measurements derived from navigation satellite signals

A Cloud-native Approach for Processing of Crowdsourced GNSS Observations and Machine Learning at Scale: A Case Study from the CAMALIOT Project

The era of modern smartphones, running on Android version 7.0 and higher, facilitates nowadays acquisition of raw dual-frequency multi-constellation GNSS observations. This paves the way for GNSS community data to be potentially exploited for precise positioning, GNSS reflectometry or geoscience applications at large. The continuously expanding global GNSS infrastructure along with the enormous volume of prospective GNSS community data bring, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest. In addition, such large datasets cannot be managed manually anymore, leading thus to the need for fully automated and sophisticated data processing pipelines. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) was an ESA NAVISP Element 1 project (NAVISP-EL1-038.2) with activities aiming to address the aforementioned points related to GNSS community data and their exploitation for scientific applications with the use of Machine Learning (ML). This contribution provides an overview of the CAMALIOT project with information on the designed and implemented cloud-native software for GNSS processing and ML at scale, developed Android application for retrieving GNSS observations from the modern generation of smartphones through dedicated crowdsourcing campaigns, related data ingestion and processing, and GNSS analysis concerning both conventional and smartphone GNSS observations. With the use of the developed GNSS engine employing an Extended Kalman Filter, example processing results related to the Zenith Total Delay (ZTD) and Slant Total Electron Content (STEC) are provided based on the analysis of observations collected with geodetic-grade GNSS receivers and from local measurement sessions involving Xiaomi Mi 8 that collected GNSS observations using the developed Android application. For smartphone observations, ZTD is derived in a differential manner based on a single-frequency double-difference approach employing GPS and Galileo observations, whereas satellite-specific STEC time series are obtained through carrier-to-code leveling based on the geometry-free linear combination of observations from both GPS and Galileo constellations. Although the ZTD and STEC time series from smartphones were derived on a demonstration basis, a rather good level of consistency of such estimates with respect to the reference time series was found. For the considered periods, the RMS of differences between the derived smartphone-based time series of differential zenith wet delay and reference values were below 3.1 mm. In terms of satellite-specific STEC time series expressed with respect to the reference STEC time series, RMS of the offset-reduced differences below 1.2 TECU was found. Smartphone-based observations require special attention including additional processing steps and a dedicated parameterization in order to be able to acquire reliable atmospheric estimates. Although with lower measurement quality compared to traditional sources of GNSS data, an augmentation of ground-based networks of fixed high-end GNSS receivers with GNSS-capable smartphones would however, form an interesting source of complementary information for various studies relying on GNSS observations

Contributions to the foundations of a safety case for the use of GNSS in railway environments

The use of GNSS in the railways for passenger information services and selective door opening is already commonplace but the advancement of this increasingly popular navigation technique into safety of life rail applications has been hindered by the unknown level of measurement error caused by the local rail environment, especially that due to multipath. Current state of the art receiver technologies are discussed along with the additional advantages of signal differencing using local base stations. Limiting factors for hardware in a kinematic environment are also discussed and specific examples to the rail environment highlighted. Safety critical analysis techniques such as FMEA, HAZOP and FTA are reviewed to illustrate the evaluation of safety integrity values and the possibility of system risk, leading to the formation of a structured safety case. Three main data sets from electrified, rural and urban rail environments have been collected using dual frequency geodetic receivers in order to enable analysis of multipath effects in normal railway operations. The code and phase data have been combined to compute fluctuations in multipath errors and these have been used to characterise this effect in both space and time. Where phase positioning is possible comparisons with standard code-based positions have been made to assess the overall quality of the type of GNSS positioning expected to be operationally-viable on the railways. Experiments have also been undertaken to evaluate the possible effects of electromagnetic radiation from overhead cables used to power the trains. Finally, the ways in which the results of these experiments can be used to help build a safety case for the use of GNSS on the railways are discussed. Overall it is concluded that it is unlikely that multipath errors or electromagnetic interference will be the limiting factors in utilising GNSS for safety-critical railway applications

Cooperative Relative Positioning for Vehicular Environments

Fahrerassistenzsysteme sind ein wesentlicher Baustein zur Steigerung der Sicherheit im Straßenverkehr. Vor allem sicherheitsrelevante Applikationen benötigen eine genaue Information über den Ort und der Geschwindigkeit der Fahrzeuge in der unmittelbaren Umgebung, um mögliche Gefahrensituationen vorherzusehen, den Fahrer zu warnen oder eigenständig einzugreifen. Repräsentative Beispiele für Assistenzsysteme, die auf eine genaue, kontinuierliche und zuverlässige Relativpositionierung anderer Verkehrsteilnehmer angewiesen sind, sind Notbremsassitenten, Spurwechselassitenten und Abstandsregeltempomate. Moderne Lösungsansätze benutzen Umfeldsensorik wie zum Beispiel Radar, Laser Scanner oder Kameras, um die Position benachbarter Fahrzeuge zu schätzen. Dieser Sensorsysteme gemeinsame Nachteile sind deren limitierte Erfassungsreichweite und die Notwendigkeit einer direkten und nicht blockierten Sichtlinie zum Nachbarfahrzeug. Kooperative Lösungen basierend auf einer Fahrzeug-zu-Fahrzeug Kommunikation können die eigene Wahrnehmungsreichweite erhöhen, in dem Positionsinformationen zwischen den Verkehrsteilnehmern ausgetauscht werden. In dieser Dissertation soll die Möglichkeit der kooperativen Relativpositionierung von Straßenfahrzeugen mittels Fahrzeug-zu-Fahrzeug Kommunikation auf ihre Genauigkeit, Kontinuität und Robustheit untersucht werden. Anstatt die in jedem Fahrzeug unabhängig ermittelte Position zu übertragen, werden in einem neuartigem Ansatz GNSS-Rohdaten, wie Pseudoranges und Doppler-Messungen, ausgetauscht. Dies hat den Vorteil, dass sich korrelierte Fehler in beiden Fahrzeugen potentiell herauskürzen. Dies wird in dieser Dissertation mathematisch untersucht, simulativ modelliert und experimentell verifiziert. Um die Zuverlässigkeit und Kontinuität auch in "gestörten" Umgebungen zu erhöhen, werden in einem Bayesischen Filter die GNSS-Rohdaten mit Inertialsensormessungen aus zwei Fahrzeugen fusioniert. Die Validierung des Sensorfusionsansatzes wurde im Rahmen dieser Dissertation in einem Verkehrs- sowie in einem GNSS-Simulator durchgeführt. Zur experimentellen Untersuchung wurden zwei Testfahrzeuge mit den verschiedenen Sensoren ausgestattet und Messungen in diversen Umgebungen gefahren. In dieser Arbeit wird gezeigt, dass auf Autobahnen, die Relativposition eines anderen Fahrzeugs mit einer Genauigkeit von unter einem Meter kontinuierlich geschätzt werden kann. Eine hohe Zuverlässigkeit in der longitudinalen und lateralen Richtung können erzielt werden und das System erweist 90% der Zeit eine Unsicherheit unter 2.5m. In ländlichen Umgebungen wächst die Unsicherheit in der relativen Position. Mit Hilfe der on-board Sensoren können Fehler bei der Fahrt durch Wälder und Dörfer korrekt gestützt werden. In städtischen Umgebungen werden die Limitierungen des Systems deutlich. Durch die erschwerte Schätzung der Fahrtrichtung des Ego-Fahrzeugs ist vor Allem die longitudinale Komponente der Relativen Position in städtischen Umgebungen stark verfälscht.Advanced driver assistance systems play an important role in increasing the safety on today's roads. The knowledge about the other vehicles' positions is a fundamental prerequisite for numerous safety critical applications, making it possible to foresee critical situations, warn the driver or autonomously intervene. Forward collision avoidance systems, lane change assistants or adaptive cruise control are examples of safety relevant applications that require an accurate, continuous and reliable relative position of surrounding vehicles. Currently, the positions of surrounding vehicles is estimated by measuring the distance with e.g. radar, laser scanners or camera systems. However, all these techniques have limitations in their perception range, as all of them can only detect objects in their line-of-sight. The limited perception range of today's vehicles can be extended in future by using cooperative approaches based on Vehicle-to-Vehicle (V2V) communication. In this thesis, the capabilities of cooperative relative positioning for vehicles will be assessed in terms of its accuracy, continuity and reliability. A novel approach where Global Navigation Satellite System (GNSS) raw data is exchanged between the vehicles is presented. Vehicles use GNSS pseudorange and Doppler measurements from surrounding vehicles to estimate the relative positioning vector in a cooperative way. In this thesis, this approach is shown to outperform the absolute position subtraction as it is able to effectively cancel out common errors to both GNSS receivers. This is modeled theoretically and demonstrated empirically using simulated signals from a GNSS constellation simulator. In order to cope with GNSS outages and to have a sufficiently good relative position estimate even in strong multipath environments, a sensor fusion approach is proposed. In addition to the GNSS raw data, inertial measurements from speedometers, accelerometers and turn rate sensors from each vehicle are exchanged over V2V communication links. A Bayesian approach is applied to consider the uncertainties inherently to each of the information sources. In a dynamic Bayesian network, the temporal relationship of the relative position estimate is predicted by using relative vehicle movement models. Also real world measurements in highway, rural and urban scenarios are performed in the scope of this work to demonstrate the performance of the cooperative relative positioning approach based on sensor fusion. The results show that the relative position of another vehicle towards the ego vehicle can be estimated with sub-meter accuracy in highway scenarios. Here, good reliability and 90% availability with an uncertainty of less than 2.5m is achieved. In rural environments, drives through forests and towns are correctly bridged with the support of on-board sensors. In an urban environment, the difficult estimation of the ego vehicle heading has a mayor impact in the relative position estimate, yielding large errors in its longitudinal component

Proceedings of the Sixth General Meeting of the International VLBI Service for Geodesy and Astrometry

This volume is the proceedings of the sixth General Meeting of the International VLBI Service for Geodesy and Astrometry (IVS), held in Hobart, Tasmania, Australia, February 7-13, 2010. The contents of this volume also appear on the IVS Web site at http://ivscc.gsfc.nasa.gov/publications/gm2010. The keynote of the sixth GM was the new perspectives of the next generation VLBI system under the theme "VLBI2010: From Vision to Reality". The goal of the meeting was to provide an interesting and informative program for a wide cross-section of IVS members, including station operators, program managers, and analysts. This volume contains 88 papers. All papers were edited by the editors for usage of the English language, form, and minor content-related issues

Estimation Techniques and Mitigation Tools for Ionospheric effects on GNSS Receivers

Navigation is defined as the science of getting a craft or person from one place to another. The development of radio in the past century brought fort new navigation aids that enabled users, or rather their receivers, to compute their position with the help of signals from one or more radio-navigation system . The U.S. Global Positioning System (GPS) was envisioned as a satellite system for three-dimensional position and velocity determination fulfilling the following key attributes: global coverage, continuous/all weather operation, ability to serve high-dynamic platforms, and high accuracy. It represents the fruition of several technologies, which matured and came together in the second half of the 20th century. In particular, stable space-born platforms, ultra-stable atomic frequency standards, spread spectrum signaling, and microelectronics are the key developments in the realization and success of GPS. While GPS was under development, the Soviet Union undertook to develop a similar system called GLObalnaya NAvigatsionnaya Sputnikovaya Sistema (GLONASS). Both GLONASS and GPS were designed primarily for the military, but have transitioned in the past decades towards providing civilian and Safety-of-Life services as well. Other Global Navigation Satellite Systems (GNSS) are now being developed and deployed by governments, international consortia, and commercial interests. Among these are the European system Galileo and the Chinese system Beidou. Other regional systems are the Japanese Quasi-Zenith Satellite System and the Indian Gagan. GNSS have become a crucial component in countless modern systems, e.g. in telecommunication, navigation, remote sensing, precise agriculture, aviation and timing. One of the main threats to the reliable and safe operation of GNSS are the variable propagation conditions encountered by GNSS signals as they pass through the upper atmosphere of the Earth. In particular, irregular concentration of electrons in the ionosphere induce fast fluctuations in the amplitude and phase of GNSS signals called scintillations. The latter can greatly degrade the performance of GNSS receivers, with consequent economical impacts on service providers and users of high performance applications. New GNSS navigation signals and codes are expected to help mitigate such effects, although to what degree is still unknown. Furthermore, these new technologies will only come on line incrementally over the next decade as new GNSS satellites become operational. In the meantime, GPS users who need high performance navigation solution, e.g., offshore drilling companies, might be forced to postpone operations for which precision position knowledge is required until the ionospheric disturbances are over. For this reason continuous monitoring of scintillations has become a priority in order to try to predict its occurrence. Indeed, it is a growing scientific and industrial activity. However, Radio Frequency (RF) Interference from other telecommunication systems might threaten the monitoring of scintillation activity. Currently, the majority of the GNSS based application are highly exposed to unintentional or intentional interference issues. The extremely weak power of the GNSS signals, which is actually completely buried in the noise floor at the user receiver antenna level, puts interference among the external error contributions that most degrade GNSS performance. It is then of interest to study the effects these external systems may have on the estimation of ionosphere activity with GNSS. In this dissertation, we investigate the effect of propagation issues in GNSS, focusing on scintillations, interference and the joint effect of the two phenomena