33 research outputs found

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

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    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

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    Menci贸n Internacional en el t铆tulo 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: 鈥揷ode pseudorange, which is a measure of the time difference between the signal鈥檚 emission and reception at the satellite and receiver, respectively, scaled by the speed of light; 鈥揷arrier 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鈥檚 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 informaci贸n de navegaci贸n es un elemental fundamental para el funcionamiento de sistemas de transporte inteligentes y plataformas rob贸ticas. Entre las tecnolog铆as existentes, los Sistemas Globales de Navegaci贸n por Sat茅lite (GNSS) se han consolidado como la piedra angular para la navegaci贸n en exteriores, dando acceso a localizaci贸n y sincronizaci贸n temporal a una escala global, irrespectivamente de la condici贸n meteorol贸gica. GNSS es el t茅rmino gen茅rico que define una constelaci贸n de sat茅lites que transmiten se帽ales de radio, usadas primordinalmente para proporcionar informaci贸n de distancia. Por lo tanto, la operatibilidad y funcionamiento de los futuros sistemas aut贸nomos pende de nuestra capacidad para explotar GNSS y estimar soluciones de navegaci贸n robustas y precisas. Las se帽ales GNSS permiten dos tipos de observaciones de alcance: 鈥損seudorangos de c贸digo, que miden el tiempo transcurrido entre la emisi贸n de las se帽ales en los sat茅lites y su acquisici贸n en la tierra por parte de un receptor; 鈥損seudorangos de fase de portadora, que miden la fase de la onda sinusoide que portan dichas se帽ales y el n煤mero acumulado de ciclos completos. Los pseudorangos de c贸digo proporcionan una medida inequ铆voca de la distancia entre los sat茅lites y el receptor, con una precisi贸n de dec铆metros cuando no se tienen en cuenta los retrasos atmosf茅ricos y los desfases del reloj. En contraposici贸n, las observaciones de la portadora son super precisas, alcanzando el mil铆metro de exactidud, a expensas de ser ambiguas por un n煤mero entero y desconocido de ciclos. Por ende, el alcanzar la m谩xima precisi贸n con GNSS queda condicionado al uso de las medidas de fase de la portadora, lo cual implica unos problemas de estimaci贸n de elevada complejidad. Esta tesis versa sobre la teor铆a de estimaci贸n relacionada con la provisi贸n de navegaci贸n precisa basada en la fase de la portadora, especialmente para veh铆culos que transitan escenarios donde las se帽ales no se propagan f谩cilmente, como es el caso de las ciudades. Para ello, primero se aborda la m谩xima efectividad del problema de localizaci贸n, proponiendo cotas inferiores para el procesamiento de la se帽al en el receptor y para el problema de estimaci贸n mixto (es decir, cuando las inc贸gnitas pertenecen al espacio de n煤meros reales y enteros). En segundo lugar, se consideran las configuraciones multiantena para el c谩lculo de la orientaci贸n de un veh铆culo, presentando un nuevo modelo para la estimaci贸n conjunta de posici贸n y rumbo, y proponiendo estimadores deterministas y recursivos basados en la teor铆a de Lie. Por 煤ltimo, se explora el marco de la estad铆stica robusta para proporcionar nuevas soluciones de navegaci贸n precisa, capaces de hacer frente a los ruidos at铆picos.Programa de Doctorado en Ciencia y Tecnolog铆a Inform谩tica por la Universidad Carlos III de MadridPresidente: Jos茅 Manuel Molina L贸pez.- Secretario: Giorgi Gabriele.- Vocal: Fabio Dovi

    Robust GNSS Carrier Phase-based Position and Attitude Estimation

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    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

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    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

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    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鈥檚 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

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    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

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    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鈥檚 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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    [no abstract

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

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    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
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