Theses: BAYESIAN SIGNAL PROCESSING TECHNIQUES FOR GNSS RECEIVERS: FROM MULTIPATH MITIGATION TO POSITIONING P. Closas GómezAbstractThis dissertation deals with the design of satellitebased navigation receivers. The term Global Navigation Satellite Systems (GNSS) refers to those navigation systems based on a constellation of satellites, which emit ranging signals useful for positioning. Although the american GPS is probably the most popular, the european contribution (Galileo) will be operative soon. Other global and regional systems exist, all with the same objective: aid user's positioning. Initially, the thesis provides the stateoftheart in GNSS: navigation signals structure and receiver architecture. The design of a GNSS receiver consists of a number of functional blocks. From the antenna to the final position calculation, the design poses challenges in many research areas. Although the Radio Frequency chain of the receiver is commented in the thesis, the main objective of the dissertation is on the signal processing algorithms applied after signal digitation. These algorithms can be divided into two: synchronization and positioning. This classification corresponds to the two main processes typically performed by a GNSS receiver. First, the relative distance between the receiver and the set of visible satellites is estimated. These distances are calculated after estimating the delay suffered by the signal traveling from its emission at the corresponding satellite to its reception at the receiver's antenna. Estimation and tracking of these parameters is performed by the synchronization algorithm. After the relative distances to the satellites are estimated, the positioning algorithm starts its operation. Positioning is typically performed by a process referred to as trilateration: intersection of a set of spheres centered at the visible satellites and with radii the corresponding relative distances. Therefore, synchronization and positioning are processes performed sequentially and in parallel. The thesis contributes to both topics, as expressed by the subtitle of the dissertation. On the one hand, the thesis delves into the use of Bayesian filtering for the tracking of synchronization parameters (timedelays, Dopplershifts and carrierphases) of the received signal. One of the main sources of error in high precision GNSS receivers is the presence of multipath replicas apart from the lineofsight signal (LOSS). Wherefore the algorithms proposed in this part of the thesis aim at mitigating the multipath effect on synchronization estimates. The dissertation provides an introduction to the basics of Bayesian filtering, including a compendium of the most popular algorithms. Particularly, Particle Filters (PF) are studied as one of the promising alternatives to deal with nonlinear/nonGaussian systems. PF are a set of simulationbased algorithms, based on MonteCarlo methods. PF provide a discrete characterization of the posterior distribution of the system. Conversely to other simulationbased methods, PF are supported by convergence results which make them attractive in cases where the optimal solution cannot be analytically found. In that vein, a PF that incorporates a set of features to enhance its performance and robustness with a reduced number of particles is proposed. First, the linear part of the system is optimally handled by a Kalman Filter (KF), procedure referred to as RaoBlackwellization. The latter causes a reduction on the variance of the particles and, thus, a reduction on the number of required particles to attain a given accuracy when characterizing the posterior distribution. A second feature is the design of an importance density function (from which particles are generated) close to the optimal, not available in general. The selection of this function is typically a key issue in PF designs. The dissertation proposes an approximation of the optimal importance function using Laplace's method. In parallel, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) algorithms are considered, comparing these algorithms with the proposed PF by computer simulations. On the other hand, a novel point of view in the positioning problem constitutes one of the original contributions of the thesis. Whereas conventional receivers operate in a twosteps procedure (synchronization and positioning), the proposal of the thesis is a Direct Position Estimation (DPE) from the digitized signal. Considering the novelty of the approach, the dissertation provides both qualitative and quantitative motivations for the use of DPE instead of the conventional twosteps approach. DPE is studied following the Maximum Likelihood (ML) principle and an algorithm based on the Accelerated Random Search (ARS) is considered for a practical implementation of the derived estimator. Computer simulation results carried show the robustness of DPE in scenarios where the conventional approach fails, for instance in multipathrich scenarios. One of the conclusions of the thesis is that joint processing of satellite's signals provides enhance positioning performances, due to the independent propagation channels between satellite links. The dissertation also presents the extension of DPE to the Bayesian framework: Bayesian DPE (BDPE). BDPE maintains DPE's philosophy, including the possibility of accounting for sources of side/prior information. Some examples are given, such as the use of Inertial Measurement Systems and atmospheric models. Nevertheless, we have to keep in mind that the list is only limited by imagination and the particular applications were BDPE is implemented. Finally, the dissertation studied the theoretical lower bounds of accuracy of GNSS receivers. Some of those limits were already known, others see the light as a result of the research reported in the dissertation. The CramérRao Bound (CRB) is the theoretical lower bound of accuracy of any unbiased estimator of a parameter. The dissertation recalls the CRB of synchronization parameters, result already known. A novel contribution of the thesis is the derivation of the CRB of the position estimator for either conventional and DPE approaches. These results provide an asymptotical comparison of both GNSS positioning approaches. Similarly, the CRB of synchronization parameters for the Bayesian case (Posterior CramérRao Bound, PCRB) is given, used as a fundamental limit of the Bayesian filters proposed in the thesis. 
