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

Forward and Inverse Modeling of GPS Multipath for Snow Monitoring

Snowpacks provide reservoirs of freshwater, storing solid precipitation and delaying runoff to be released later in the spring and summer when it is most needed. The goal of this dissertation is to develop the technique of GPS multipath reflectometry (GPS-MR) for ground-based measurement of snow depth. The phenomenon of multipath in GPS constitutes the reception of reflected signals in conjunction with the direct signal from a satellite. As these coherent direct and reflected signals go in and out of phase, signal-to-noise ratio (SNR) exhibits peaks and troughs that can be related to land surface characteristics. In contrast to other GPS reflectometry modes, in GPS-MR the poorly separated composite signal is collected utilizing a single antenna and correlated against a single replica. SNR observations derived from the newer L2-frequency civilian GPS signal (L2C) are used, as recorded by commercial off-the-shelf receivers and geodetic-quality antennas in existing GPS sites. I developed a forward/inverse approach for modeling GPS multipath present in SNR observations. The model here is unique in that it capitalizes on known information about the antenna response and the physics of surface scattering to aid in retrieving the unknown snow conditions in the antenna surroundings. This physically-based forward model is utilized to simulate the surface and antenna coupling. The statistically-rigorous inverse model is considered in two parts. Part I (theory) explains how the snow characteristics are parameterized; the observation/parameter sensitivity; inversion errors; and parameter uncertainty, which serves to indicate the sensing footprint where the reflection originates. Part II (practice) applies the multipath model to SNR observations and validates the resulting GPS retrievals against independent in situ measurements during a 1-3 year period in three different environments - grasslands, alpine, and forested. The assessment yields a correlation of 0.98 and an RMS error of 6-8 cm, with the GPS under-estimating in situ snow depth by approximately 15%. GPS daily site averages were found effective in mitigating random noise without unduly smoothing the sharp transitions as captured in new snow events. This work corroborates the readiness of quality-controlled GPS-MR for snow depth monitoring, reinforcing its maturity for operational usage

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

Robust GNSS Carrier Phase-based Position and Attitude Estimation Theory and Applications

Mención Internacional en el título de doctorNavigation information is an essential element for the functioning of robotic platforms and intelligent transportation systems. Among the existing technologies, Global Navigation Satellite Systems (GNSS) have established as the cornerstone for outdoor navigation, allowing for all-weather, all-time positioning and timing at a worldwide scale. GNSS is the generic term for referring to a constellation of satellites which transmit radio signals used primarily for ranging information. Therefore, the successful operation and deployment of prospective autonomous systems is subject to our capabilities to support GNSS in the provision of robust and precise navigational estimates. GNSS signals enable two types of ranging observations: –code pseudorange, which is a measure of the time difference between the signal’s emission and reception at the satellite and receiver, respectively, scaled by the speed of light; –carrier phase pseudorange, which measures the beat of the carrier signal and the number of accumulated full carrier cycles. While code pseudoranges provides an unambiguous measure of the distance between satellites and receiver, with a dm-level precision when disregarding atmospheric delays and clock offsets, carrier phase measurements present a much higher precision, at the cost of being ambiguous by an unknown number of integer cycles, commonly denoted as ambiguities. Thus, the maximum potential of GNSS, in terms of navigational precision, can be reach by the use of carrier phase observations which, in turn, lead to complicated estimation problems. This thesis deals with the estimation theory behind the provision of carrier phase-based precise navigation for vehicles traversing scenarios with harsh signal propagation conditions. Contributions to such a broad topic are made in three directions. First, the ultimate positioning performance is addressed, by proposing lower bounds on the signal processing realized at the receiver level and for the mixed real- and integer-valued problem related to carrier phase-based positioning. Second, multi-antenna configurations are considered for the computation of a vehicle’s orientation, introducing a new model for the joint position and attitude estimation problems and proposing new deterministic and recursive estimators based on Lie Theory. Finally, the framework of robust statistics is explored to propose new solutions to code- and carrier phase-based navigation, able to deal with outlying impulsive noises.La información de navegación es un elemental fundamental para el funcionamiento de sistemas de transporte inteligentes y plataformas robóticas. Entre las tecnologías existentes, los Sistemas Globales de Navegación por Satélite (GNSS) se han consolidado como la piedra angular para la navegación en exteriores, dando acceso a localización y sincronización temporal a una escala global, irrespectivamente de la condición meteorológica. GNSS es el término genérico que define una constelación de satélites que transmiten señales de radio, usadas primordinalmente para proporcionar información de distancia. Por lo tanto, la operatibilidad y funcionamiento de los futuros sistemas autónomos pende de nuestra capacidad para explotar GNSS y estimar soluciones de navegación robustas y precisas. Las señales GNSS permiten dos tipos de observaciones de alcance: –pseudorangos de código, que miden el tiempo transcurrido entre la emisión de las señales en los satélites y su acquisición en la tierra por parte de un receptor; –pseudorangos de fase de portadora, que miden la fase de la onda sinusoide que portan dichas señales y el número acumulado de ciclos completos. Los pseudorangos de código proporcionan una medida inequívoca de la distancia entre los satélites y el receptor, con una precisión de decímetros cuando no se tienen en cuenta los retrasos atmosféricos y los desfases del reloj. En contraposición, las observaciones de la portadora son super precisas, alcanzando el milímetro de exactidud, a expensas de ser ambiguas por un número entero y desconocido de ciclos. Por ende, el alcanzar la máxima precisión con GNSS queda condicionado al uso de las medidas de fase de la portadora, lo cual implica unos problemas de estimación de elevada complejidad. Esta tesis versa sobre la teoría de estimación relacionada con la provisión de navegación precisa basada en la fase de la portadora, especialmente para vehículos que transitan escenarios donde las señales no se propagan fácilmente, como es el caso de las ciudades. Para ello, primero se aborda la máxima efectividad del problema de localización, proponiendo cotas inferiores para el procesamiento de la señal en el receptor y para el problema de estimación mixto (es decir, cuando las incógnitas pertenecen al espacio de números reales y enteros). En segundo lugar, se consideran las configuraciones multiantena para el cálculo de la orientación de un vehículo, presentando un nuevo modelo para la estimación conjunta de posición y rumbo, y proponiendo estimadores deterministas y recursivos basados en la teoría de Lie. Por último, se explora el marco de la estadística robusta para proporcionar nuevas soluciones de navegación precisa, capaces de hacer frente a los ruidos atípicos.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Manuel Molina López.- Secretario: Giorgi Gabriele.- Vocal: Fabio Dovi

Reduction of initial convergence period in GPS PPP data processing

Precise Point Positioning (PPP) has become a popular technique to process data from GPS receivers by applying precise satellite orbit and clock information, along with other minor corrections to produce cm to dm-level positioning. Although PPP presents definite advantages such as operational flexibility and cost effectiveness for users, it requires 15-25 minutes initialization period as carrier-phase ambiguities converge to constant values and the solution reaches its optimal precision. Pseudorange multipath and noise are the largest remaining unmanaged errors source in PPP. It is proposed that by reducing these effects carrier-phase ambiguities will reach the correct steady state at an earlier time, thus reducing the convergence period of PPP. Given this problem, this study seeks to improve management of these pseudorange errors. The well-known multipath linear combination was used in two distinct ways: 1) to directly correct the raw pseudorange observables, and 2) to stochastically de-weight the pseudorange observables. Corrections to the observables were made in real-time using data from the day before, and post-processed using data from the same day. Post-processing has shown 4 7% improvement in the rate of convergence, as the pseudorange multipath and noise were effectively mitigated. A 36% improvement in the rate of convergence was noted when the pseudorange measurements were stochastically de-weighting using the multipath observable. The strength of this model is that it allows for real-time compensation of the effects of the pseudorange multipath and noise in the stochastic model