A Novel Carrier Loop Based on Adaptive LM-QN Method in GNSS Receivers

A well-designed carrier tracking loop in a receiver of the Global Navigation Satellite System (GNSS) is the premise of accurate positioning and navigation in an aircraft-based surveying and mapping system. To deal with the problems of Doppler estimation in high-dynamic maneuvers, the interest on maximum-likelihood estimation (MLE) is increasing among the academic community. Levenberg-Marquardt (LM) method is usually regarded as an effective and promising approach to obtain the solution of MLE, but the computation of Hessian matrix loads a great burden on the algorithm. Besides, a poor performance on convergency in final iterations is the common failing of LM implementations. To solve these problems, an LM method based on Gauss-Newton and a Quasi-Newton (QN) method based on Hessian approximation are derived, making the computation cost of Hessian decline from O(N) to O(1). Then, on the basis of these two methods, a closed carrier loop with adaptive LM-QN algorithm is further proposed which can switch between LM and QN adaptively according to a damping parameter. Besides, an ideal LM with super-linear convergence (SLM) is constructed and proved as a reference of the convergence analysis. Finally, through the analyses and experiments using aircraft data, the improvements on computation cost and convergence are verified. Compared with scalar tracking and vector tracking, results indicate a magnitude increase in the precision of LM-QN loop, even though more computation counts are needed by LM-QN.Peer reviewe

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

Robust GNSS Carrier Phase-based Position and Attitude Estimation

Navigation 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

Robust Positioning in the Presence of Multipath and NLOS GNSS Signals

GNSS signals can be blocked and reflected by nearby objects, such as buildings, walls, and vehicles. They can also be reflected by the ground and by water. These effects are the dominant source of GNSS positioning errors in dense urban environments, though they can have an impact almost anywhere. Non- line-of-sight (NLOS) reception occurs when the direct path from the transmitter to the receiver is blocked and signals are received only via a reflected path. Multipath interference occurs, as the name suggests, when a signal is received via multiple paths. This can be via the direct path and one or more reflected paths, or it can be via multiple reflected paths. As their error characteristics are different, NLOS and multipath interference typically require different mitigation techniques, though some techniques are applicable to both. Antenna design and advanced receiver signal processing techniques can substantially reduce multipath errors. Unless an antenna array is used, NLOS reception has to be detected using the receiver's ranging and carrier-power-to-noise-density ratio (C/N0) measurements and mitigated within the positioning algorithm. Some NLOS mitigation techniques can also be used to combat severe multipath interference. Multipath interference, but not NLOS reception, can also be mitigated by comparing or combining code and carrier measurements, comparing ranging and C/N0 measurements from signals on different frequencies, and analyzing the time evolution of the ranging and C/N0 measurements

GNSS Integrity Monitoring assisted by Signal Processing techniques in Harsh Environments

The Global Navigation Satellite Systems (GNSS) applications are growing and more pervasive in the modern society. The presence of multi-constellation GNSS receivers able to use signals coming from different systems like the american Global Positioning System (GPS), the european Galileo, the Chinese Beidou and the russian GLONASS, permits to have more accuracy in position solution. All the receivers provide always more reliable solution but it is important to monitor the possible presence of problems in the position computation. These problems could be caused by the presence of impairments given by unintentional sources like multipath generated by the environment or intentional sources like spoofing attacks. In this thesis we focus on design algorithms at signal processing level used to assist Integrity operations in terms of Fault Detection and Exclusion (FDE). These are standalone algorithms all implemented in a software receiver without using external information. The first step was the creation of a detector for correlation distortion due to the multipath with his limitations. Once the detection is performed a quality index for the signal is computed and a decision about the exclusion of a specific Satellite Vehicle (SV) is taken. The exclusion could be not feasible so an alternative approach could be the inflation of the variance of the error models used in the position computation. The quality signal can be even used for spoofinng applications and a novel mitigation technique is developed and presented. In addition, the mitigation of the multipath can be reached at pseudoranges level by using new method to compute the position solution. The main contributions of this thesis are: the development of a multipath, or more in general, impairments detector at signal processing level; the creation of an index to measure the quality of a signal based on the detector’s output; the description of a novel signal processing method for detection and mitigation of spoofing effects, based on the use of linear regression algorithms; An alternative method to compute the Position Velocity and Time (PVT) solution by using different well known algorithms in order to mitigate the effects of the multipath on the position domain

Attack resilient GPS based timing for phasor measurement units using multi-receiver direct time estimation

Modern power distribution systems are incorporating Phasor Measurement Units (PMUs) to measure the instantaneous voltage and current phasors at different nodes in the power grid. These PMUs depend on Global Positioning Systems (GPS) for precise time and synchronization. However, GPS civil signals are vulnerable to external attacks because of its low power and unencrypted signal structure. Therefore, there is a need for the development of attack resilient GPS time transfer techniques to ensure power grid stability. To counteract these adverse effects, we propose an innovative Multi-Receiver Direct Time Estimation (MR-DTE) algorithm by utilizing the measurements from multiple GPS receivers driven by a common clock. The raw GPS signals from each receiver are processed using a robust signal processing technique known as Direct Time Estimation (DTE). DTE directly correlates the received GPS signal with the corresponding signal replica for each of the pre-generated set of clock states. The optimal set of clock candidates is then determined by maximum likelihood estimation. We further leverage the known geographical diversity of multiple receivers and apply Kalman Filter to obtain robust GPS timing. We evaluate the improved robustness of our MR-DTE algorithm against external timing attacks based on GPS field experiments. In addition, we design a verification and validation power grid testbed using Real-Time Digital Simulator (RTDS) to demonstrate the impact of jamming, meaconing (i.e., record-andreplay attack) and satellite data-level anomalies on PMUs. Later, we utilize our power grid testbed to validate the attack-resilience of our proposed MR-DTE algorithm in comparison to the existing techniques such as traditional scalar tracking and Position-Information-Aided Vector Tracking

Estimation of tropospheric wet delay from GNSS measurements

The determination of the zenith wet delay (ZWD) component can be a difficult task due to the dynamic nature of atmospheric water vapour. However, precise estimation of the ZWD is essential for high-precision Global Navigation Satellite System (GNSS) applications such as real-time positioning and Numerical Weather Prediction (NWP) modelling.The functional and stochastic models that can be used for the estimation of the tropospheric parameters from GNSS measurements are presented and discussed in this study. The focus is to determine the ZWD in an efficient manner in static mode. In GNSS, the estimation of the ZWD is directly impacted by the choice of stochastic model used in the estimation process. In this thesis, the rigorous Minimum Norm Quadratic Unbiased Estimation (MINQUE) method was investigated and compared with traditional models such as the equal-weighting model (EWM) and the elevationangle dependent model (EADM). A variation of the MINQUE method was also introduced. A simulation study of these models resulted in MINQUE outperforming the other stochastic models by at least 36% in resolving the height component. However, this superiority did not lead to better ZWD estimates. In fact, the EADM provided the most accurate set of ZWD estimates among all the models tested. The EADM also yielded the best ZWD estimates in the real data analyses for two independent baselines in Australia and in Europe, respectively.The study also assessed the validity of a baseline approach, with a reduced processing window size, to provide good ZWD estimates at Continuously Operating Reference Stations (CORS) in an efficient manner. Results show that if the a-priori station coordinates are accurately known, the baseline approach, along with a 2-hour processing window, can produce ZWD estimates that are statistically in good agreement with the estimates from external sources such as the radiosonde (RS), water vapour radiometer (WVR) and International GNSS Service (IGS) solutions. Resolving the ZWD from GNSS measurements in such a timely manner can aid NWP model in providing near real-time weather forecasts in the data assimilation process.In the real-time kinematic modelling of GNSS measurements, the first-order Gauss- Markov (GM) autocorrelation model is commonly used for the dynamic model in Kalman filtering. However, for the purpose of ZWD estimation, it was found that the GM model consistently underestimates the temporal correlations that exist among the ZWD measurements. Therefore, a new autocorrelation dynamic model is proposed in a form similar to that of a hyperbolic function. The proposed model initially requires a small number of autocorrelation estimates using the standard autocorrelation formulations. With these autocorrelation estimates, the least-squares method is then implemented to solve for the model’s parameter coefficients. Once solved, the model is then fully defined. The proposed model was shown to be able to follow the autocorrelation trend better than the GM model. Additionally, analysis of real data at an Australian IGS station has showed the proposed model performed better than the random-walk model, and just as well as the GM model. The proposed model was able to provide near real-time (i.e. 30 seconds interval) ZTD estimates to within 2 cm accuracy on average.The thesis also included an investigation into the several interpolation models for estimating missing ZWD observations that may take place during temporary breakdowns of GNSS stations, or malfunctions of RS and WVR equipments. Results indicated marginal differences between the polynomial regression models, linear interpolation, fast-Fourier transform and simple Kriging methods. However, the linear interpolation method, which is dependent on the two most recent data points, is preferable due to its simplicity. This result corresponded well with the autocorrelation analysis of the ZWD estimates where significant temporal correlations were observed for at most two hours.The study concluded with an evaluation of several trend and smoothing models to determine the best models for predicting ZWD estimates, which can help improve real-time kinematic (RTK) positioning by mitigating the tropospheric effect. The moving average (MA) and the single-exponential smoothing (SES) models were shown to be the best-performing prediction models overall. These two models were able to provide ZWD estimates with forecast errors of less 10% for up to 4 hours of prediction

Bayesian algorithms for mobile terminal positioning in outdoor wireless environments

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Cramer-Rao bounds in the estimation of time of arrival in fading channels

This paper computes the Cramer-Rao bounds for the time of arrival estimation in a multipath Rice and Rayleigh fading scenario, conditioned to the previous estimation of a set of propagation channels, since these channel estimates (correlation between received signal and the pilot sequence) are sufficient statistics in the estimation of delays. Furthermore, channel estimation is a constitutive block in receivers, so we can take advantage of this information to improve timing estimation by using time and space diversity. The received signal is modeled as coming from a scattering environment that disperses the signal both in space and time. Spatial scattering is modeled with a Gaussian distribution and temporal dispersion as an exponential random variable. The impact of the sampling rate, the roll-off factor, the spatial and temporal correlation among channel estimates, the number of channel estimates, and the use of multiple sensors in the antenna at the receiver is studied and related to the mobile subscriber positioning issue. To our knowledge, this model is the only one of its kind as a result of the relationship between the space-time diversity and the accuracy of the timing estimation.Peer ReviewedPostprint (published version

Towards secure & robust PNT for automated systems

This dissertation makes four contributions in support of secure and robust position, navigation, and timing (PNT) for automated systems. The first two relate to PNT security while the latter two address robust positioning for automated ground vehicles. The first contribution is a fundamental theory for provably-secure clock synchronization between two agents in a distributed automated system. All one-way synchronization protocols, such as those based on the Global Positioning System (GPS) and other Global Navigation Satellite Systems (GNSS), are shown to be vulnerable to man-in-the-middle delay attacks. This contribution is the first to identify the necessary and sufficient conditions for provably secure clock synchronization. The second contribution, also related to PNT security, is a three-year study of the world-wide GPS interference landscape based on data from a dual-frequency GNSS receiver operating continuously on the International Space Station (ISS). This work is the first publicly-reported space-based survey of GNSS interference, and unveils previously-unreported GNSS interference activity. The third contribution is a novel ground vehicle positioning technique that is robust to GNSS signal blockage, poor lighting conditions, and adverse weather events such as heavy rain and dense fog. The technique relies on sensors that are commonly available on automated vehicles and are insensitive to lighting and inclement weather: automotive radar, low-cost inertial measurement units (IMUs), and GNSS. Remarkably, it is shown that, given a prior radar map, the proposed technique operating on data from off-the-shelf all-weather automotive sensors can maintain sub-50-cm horizontal position accuracy during 60 min of GNSS-denied driving in downtown Austin, TX. This dissertation’s final contribution is an analysis and demonstration of the feasibility of crowd-sourced digital mapping for automated vehicles. Localization techniques, such as the one described in the previous contribution, rely on such digital maps for accuracy and robustness. A key enabler for large-scale up-to-date maps is enlisting the help of the very consumer vehicles that need the map to build and update it. A method for fusing multi-session vision data into a unified digital map is developed. The asymptotic limit of such a map’s globally-referenced position accuracy is explored for the case in which the mapping agents rely on low-cost GNSS receivers performing standard code-phase-based navigation. Experimental validation along a semi-urban route shows that low-cost consumer vehicles incrementally tighten the accuracy of the jointly-optimized digital map over time enough to support sub-lane-level positioning in a global frame of reference.Electrical and Computer Engineerin