## Bayesian Signal Processing Techniques for GNSS Receivers: from multipath mitigation to positioning (2009)

Advanced Signal Processing Techniques for Global Navigation Satellite Systems

This Dissertation addresses the synchronization problem using an array of antennas in the general framework of Global Navigation Satellite Systems (GNSS) receivers. Positioning systems are based on time delay and frequency-shift estimation of the incoming signals in the receiver side, in order to compute the user's location. Sources of accuracy degradation in satellite-based navigation systems are well-known, and their mitigation has deserved the attention of a number of researchers in latter times. While atmospheric-dependant sources (delays that depend on the ionosphere and troposphere conditions) can be greatly mitigated by differential systems external to the receiver's operation, the multipath effect is location-dependant and remains as the most important cause of accuracy degradation in time delay estimation, and consequently in position estimation, becoming a signal processing challenge. Traditional approaches to time delay estimation are often embodied in a communication systems framework. Indeed, ...

Fernandez-Prades, Carles — Universitat Politecnica de Catalunya

Estimation of Nonlinear Dynamic Systems: Theory and Applications

This thesis deals with estimation of states and parameters in nonlinear and non-Gaussian dynamic systems. Sequential Monte Carlo methods are mainly used to this end. These methods rely on models of the underlying system, motivating some developments of the model concept. One of the main reasons for the interest in nonlinear estimation is that problems of this kind arise naturally in many important applications. Several applications of nonlinear estimation are studied. The models most commonly used for estimation are based on stochastic difference equations, referred to as state-space models. This thesis is mainly concerned with models of this kind. However, there will be a brief digression from this, in the treatment of the mathematically more intricate differential-algebraic equations. Here, the purpose is to write these equations in a form suitable for statistical signal processing. The nonlinear state estimation problem is ...

Schon, Thomas — Linkopings Universitet

Particle Filters and Markov Chains for Learning of Dynamical Systems

Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and non-Gaussian state-space models. This thesis builds upon several methodological advances within these classes of Monte Carlo methods. Particular emphasis is placed on the combination of SMC and MCMC in so called particle MCMC algorithms. These algorithms rely on SMC for generating samples from the often highly autocorrelated state-trajectory. A specific particle MCMC algorithm, referred to as particle Gibbs with ancestor sampling (PGAS), is suggested. By making use of backward sampling ideas, albeit implemented in a forward-only fashion, PGAS enjoys good mixing even when using seemingly few particles in the underlying SMC sampler. This results in a computationally competitive particle MCMC algorithm. As illustrated in this thesis, PGAS is a useful tool for both ...

Lindsten, Fredrik — Linköping University

Analysis of Multipath Mitigation Techniques for Satellite-based Positioning Applications

Multipath remains a dominant source of ranging errors in any Global Navigation Satellite System (GNSS), such as the Global Positioning System (GPS) or the developing European satellite navigation system Galileo. Multipath is undesirable in the context of GNSS, since the reception of multipath can create significant distortion to the shape of the correlation function used in the time delay estimate of a Delay Locked Loop (DLL) of a navigation receiver, leading to an error in the receiver's position estimate. Therefore, in order to mitigate the impact of multipath on a navigation receiver, the multipath problem has been approached from several directions, including the development of novel signal processing techniques. Many of these techniques rely on modifying the tracking loop discriminator (i.e., the DLL and its enhanced variants) in order to make it resistant to multipath, but their performance in severe ...

Bhuiyan, Mohammad Zahidul Hasan — Tampere University of Technology

GNSS Array-based Acquisition: Theory and Implementation

This Dissertation addresses the signal acquisition problem using antenna arrays in the general framework of Global Navigation Satellite Systems (GNSS) receivers. GNSSs provide the necessary infrastructures for a myriad of applications and services that demand a robust and accurate positioning service. GNSS ranging signals are received with very low signal-to-noise ratio. Despite that the GNSS CDMA modulation offers limited protection against Radio Frequency Interferences (RFI), an interference that exceeds the processing gain can easily degrade receivers' performance or even deny completely the GNSS service. A growing concern of this problem has appeared in recent times. A single-antenna receiver can make use of time and frequency diversity to mitigate interferences, even though the performance of these techniques is compromised in the presence of wideband interferences. Antenna arrays receivers can benefit from spatial-domain processing, and thus mitigate the effects of interfering signals. ...

Arribas, Javier — Universitat Politecnica de Catalunya

Performance Analysis of Bistatic Radar and Optimization methodology in Multistatic Radar System

This work deals with the problem of calculating the Cramer-Rao lower bounds (CRLBs) for bistatic radar channels. To this purpose we exploited the relation between the Ambiguity Function (AF) and the CRLB. The bistatic CRLBs are analyzed and compared to the monostatic counterparts as a function of the bistatic geometric parameters. In the bistatic case both geometry factors and transmitted waveforms play an important role in the shape of the AF, and therefore in the estimation accuracy of the target range and velocity. In particular, the CRLBs depend on the target direction of arrival, the bistatic baseline length, and the distance between the target and the receiver. The CRLBs are then used to select the optimum bistatic channel (or set of channels) for the tracking of a radar target moving along a trajectory in a multistatic scenario and for design ...

Stinco, Pietro — Universita di Pisa

Bayesian Algorithms for Mobile Terminal Positioning in Outdoor Wireless Environments

The ability to reliably and cheaply localize mobile terminals will allow users to understand and utilize the what, where and when of the surrounding physical world. Therefore, mobile terminal location information will open novel application opportunities in many areas. The mobile terminal positioning problem is categorized into three different types according to the availability of (1) initial accurate location information and (2) motion measurement data. Location estimation refers to the mobile positioning problem when both the initial location and motion measurement data are not available. If both are available, the positioning problem is referred to as position tracking. When only motion measurements are available the problem is known as global localization. These positioning problems were solved within the Bayesian filtering framework in order to work under a common theoretical context. Filter derivation and implementation algorithms are provided with emphasis on ...

Khalaf-Allah, Mohamed — Leibniz University of Hannover

Applications for the new generations of Global Navigation Satellite Systems (GNSS) are developing rapidly and attract a great interest. Both US Global Positioning System (GPS) and European Galileo signals use Direct Sequence-Code Division Multiple Access (DS-CDMA) technology, where code and frequency synchronization are important stages at the receiver. The GNSS receivers estimate jointly the code phase and the Doppler spread through a two-dimensional searching process in time-frequency plane. Since both GPS and Galileo systems will send several signals on the same carriers, a new modulation type - the Binary Offset Carrier (BOC) modulation, has been selected. The main target of this modulation is to provide a better spectral separation with the existing BPSK-modulated GPS signals, while allowing optimal usage of the available bandwidth for different GNSS signals. The BOC modulation family includes several BOC variants, such as sine BOC (SinBOC), ...

Burian, Adina — Universitat Trier

Modelling of the respiratory parameters in non-invasive ventilation

In this study, the respiratory system are modelled by three linear and one non-linear lumped parameter respiratory model, the equations of the models are driven and the parameters are estimated by using statistical signal processing methods. Linear RIC, Viscoelastic and Mead models and proposed basic non-linear RC model are used to resemble the respiratory system of the patient with Chronic Obstructive Pulmonary Disease (COPD) under non-invasive ventilation. Statistical signal processing methods such as Minimum Variance Unbiased Estimation (MVUE), Maximum Likelihood Estimation (MLE), Kalman Filter (KF), Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) are very powerful methods to estimate the parameters of the systems embedded in the unknown noise. In the first part of this thesis, artificial respiratory signals (airway flow and airway pressure) are used for the performance measurement criteria. Posterior Cramer Rao Lower Bound (PCRLB) is computed ...

Saatci, Esra — Istanbul University

Sinusodial Frequency Estimation with Applications to Ultrasound

This thesis comprises two parts. The first part deals with single carrier and multiple-carrier based frequency estimation. The second part is concerned with the application of ultrasound using the proposed estimators and introduces a novel efficient implementation of a subspace tracking technique. In the first part, the problem of single frequency estimation is initially examined, and a hybrid single tone estimator is proposed, comprising both coarse and refined estimates. The coarse estimate of the unknown frequency is obtained using the unweighted linear prediction method, and is used to remove the frequency dependence of the signal-to-noise ratio (SNR) threshold. The SNR threshold is then further reduced via a combination of using an averaging filter and an outlier removal scheme. Finally, a refined frequency estimate is formed using a weighted phase average technique. The hybrid estimator outperforms other recently developed estimators and ...

Zhang, Zhuo — Cardiff University

Sequential Bayesian Modeling of non-stationary signals

are involved until the development of Sequential Monte Carlo techniques which are also known as the particle filters. In particle filtering, the problem is expressed in terms of state-space equations where the linearity and Gaussianity requirements of the Kalman filtering are generalized. Therefore, we need information about the functional form of the state variations. In this thesis, we bring a general solution for the cases where these variations are unknown and the process distributions cannot be expressed by any closed form probability density function. Here, we propose a novel modeling scheme which is as unified as possible to cover all these problems. Therefore we study the performance analysis of our unifying particle filtering methodology on non-stationary Alpha Stable process modeling. It is well known that the probability density functions of these processes cannot be expressed in closed form, except for ...

Gencaga, Deniz — Bogazici University

In Wireless Sensor Networks (WSN), the ability of sensor nodes to know its position is an enabler for a wide variety of applications for monitoring, control, and automation. Often, sensor data is meaningful only if its position can be determined. Many WSN are deployed indoors or in areas where Global Navigation Satellite System (GNSS) signal coverage is not available, and thus GNSS positioning cannot be guaranteed. In these scenarios, WSN may be relied upon to achieve a satisfactory degree of positioning accuracy. Typically, batteries power sensor nodes in WSN. These batteries are costly to replace. Therefore, power consumption is an important aspect, being performance and lifetime ofWSN strongly relying on the ability to reduce it. It is crucial to design effective strategies to maximize battery lifetime. Optimization of power consumption can be made at different layers. For example, at the ...

Moragrega, Ana — Universitat Politecnica de Catalunya

Bayesian methods for sparse and low-rank matrix problems

Many scientific and engineering problems require us to process measurements and data in order to extract information. Since we base decisions on information, it is important to design accurate and efficient processing algorithms. This is often done by modeling the signal of interest and the noise in the problem. One type of modeling is Compressed Sensing, where the signal has a sparse or low-rank representation. In this thesis we study different approaches to designing algorithms for sparse and low-rank problems. Greedy methods are fast methods for sparse problems which iteratively detects and estimates the non-zero components. By modeling the detection problem as an array processing problem and a Bayesian filtering problem, we improve the detection accuracy. Bayesian methods approximate the sparsity by probability distributions which are iteratively modified. We show one approach to making the Bayesian method the Relevance Vector ...

Sundin, Martin — Department of Signal Processing, Royal Institute of Technology KTH

Gaussian Mixture Filters in Hybrid Positioning

Bayesian filtering is a framework for the computation of an optimal state estimate fusing different types of measurements, both current and past. Positioning, especially in urban and indoor environments, is one example of an application where the powerful mathematical framework is needed to compute as a good position estimate as possible from all kinds of measurements and information. In this work, we consider the Gaussian mixture filter, which is an approximation of the Bayesian filter. Especially, we consider filtering with just a few components, which can be computed in real-time on a mobile device. We have developed and compared different Gaussian mixture filters in different scenarios. One filter uses static solutions, which are possibly ambiguous, another extends the Unscented transformation to the Gaussian mixture framework, and some filters are based on partitioning the state space. It is also possible to ...

Ali-Loytty, Simo — Tampere University of Technology

In recent years, advances in signal processing have led the wireless sensor networks to be capable of mobility. The signal processing in a wireless sensor network differs from that of a traditional wireless network mainly in two important aspects. Unlike traditional wireless networks, in a sensor network the signal processing is performed in a fully distributed manner as the sensor measurements in a distributed fashion across the network collected. Additionally, due to the limited onboard resource of a sensor network it is essential to develop energy and bandwidth efficient signal processing algorithms. The thesis is devoted to discuss the state of the arte of algorithms commonly known as tracking algorithms. Although tracking algorithms have only been attracting research and development attention recently, already a wide literature and great variety of proposed approaches regarding the topic exist. The dissertation focus on ...

Arienzo, Loredana — University of Salerno

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