<p>A control system for autonomous landing of an unmanned aerial vehicle (UAV)with high precision has been developed. The UAV is a medium sized model he-licopter. Measurements from a GPS, a camera and a compass are fused with anextended Kalman filter for state estimation of the helicopter. Four PID-controllers,one for each control signal of the helicopter, are used for the helicopter control.During the final test flights fifteen landings were performed with an average land-ing accuracy of 35 cm.    A bias in the GPS measurements makes it impossible to land the helicopter withhigh precision using only the GPS. Therefore, a vision system using a camera anda pattern provided landing platform has been developed. The vision system givesaccurate measurement of the 6-DOF pose of the helicopter relative the platform.These measurements are used to guide the helicopter to the landing target. Inorder to use the vision system in real time, fast image processing algorithms havebeen developed. The vision system can easily match up the with the camera framerate of 30 Hz.</p> / <p>Ett kontrolsystem för att autonomt landa en modellhelikopter har utvecklats.Mätdata från en GPS, en kamera samt en kompass fusioneras med ett Extend-ed Kalman Filter för tillståndsestimering av helikoptern. Fyra PID-regulatorer,en för varje kontrolsignal på helikoptern, har används för regleringen. Under densista provflygningen gjordes tre landingar av vilken den minst lyckade slutade35 cm från målet.    På grund av en drift i GPS-mätningarna är det omöjligt att landa helikopternmed hög precision med bara en GPS. Därför har ett bildbehandlingssystem som an-vänder en kamera samt ett mönster på platformen utvecklats. Bidbehandlingssys-temet mäter positionen och orienteringen av helikoptern relativt platformen. Dessamätningar används kompensera för GPS-mätningarnas drift. Snabba bildbehan-dlingsalgoritmer har utvecklats för att kunna använda bildbehandlingssystemet irealtid. Systemet är mycket snabbare än 30 bilder per sekund vilket är kameranshastighet.</p>

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This thesis is concerned with automotive active safety, and a central theme is a new safety function called Emergency Lane Assist (ELA). Automotive safety is often categorised into passive and active safety, where passive safety is concerned with reducing the effects of accidents and active safety aims at avoiding them. ELA detects lane departure manoeuvres that are likely to result in a collision and prevents them by applying a steering wheel torque. The ELA concept is based on traffic accident statistics, i.e., it is designed to give maximum safety based on information about real life traffic accidents. The ELA function puts tough requirements on the accuracy of the information from the sensors, in particular the road shape and the position of surrounding objects, and on robust threat assessment. Several signal processing methods have been developed and evaluated in order to improve the accuracy of the sensor information, and these improvements are also analysed in how they relate to the ELA requirements. Different threat assessment methods are also studied, and a common element in both the signal processing and the threat assessment is that they are based on driver behaviour models, i.e., they utilise the fact that depending on the traffic situation, drivers are more likely to behave in certain ways than others. Most of the methods are general and can be, and hopefully also will be, applied also in other safety systems, in particular when a complete picture of the vehicle surroundings is considered, including information about road and lane shape together with the position of vehicles and infrastructure. All methods in the thesis have been evaluated on authentic sensor data from actual and relevant traffic environments.

The thesis has been developed as part of the requirements for a PhD degree at the Artificial Intelligence and Integrated Computer System division (AIICS) in the Department of Computer and Information Sciences at Linköping University.The work focuses on issues related to Unmanned Aerial Vehicle (UAV) navigation, in particular in the areas of guidance and vision-based autonomous flight in situations of short and long term GPS outage.The thesis is divided into two parts. The first part presents a helicopter simulator and a path following control mode developed and implemented on an experimental helicopter platform. The second part presents an approach to the problem of vision-based state estimation for autonomous aerial platforms which makes use of geo-referenced images for localization purposes. The problem of vision-based landing is also addressed with emphasis on fusion between inertial sensors and video camera using an artificial landing pad as reference pattern. In the last chapter, a solution to a vision-based ground object geo-location problem using a fixed-wing micro aerial vehicle platform is presented.The helicopter guidance and vision-based navigation methods developed in the thesis have been implemented and tested in real flight-tests using a Yamaha Rmax helicopter. Extensive experimental flight-test results are presented. / WITAS

Design of filters used in grid-connected inverter applications involves multiple constraints. The filter requirements are driven by tight filtering tolerances of standards such as IEEE 519-1992–IEEE Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems and IEEE 1547.2-2008–IEEE Application Guide for IEEE Std 1547, IEEE Standard for Interconnecting Distributed Resources with Electric Power Systems. Higher order LCL filters are essential to achieve these regulatory standard requirements at compact size and weight. This objective of this thesis report is to evaluate design procedures for such higher order LCL filters. The initial configuration of the third order LCL filter is decided by the frequency response of the filter. The design equations are developed in per-unit basis so results can be generalized for different applications and power levels. The frequency response is decided by IEEE specifications for high frequency current ripple at the point of common coupling. The appropriate values of L and C are then designed and constructed. Power loss in individual filter components is modeled by analytical equations and an iterative process is used to arrive at the most efficient design. Different combinations of magnetic materials (ferrite, amorphous, powder) and winding types (round wire, foil) are designed and tested to determine the most efficient design. The harmonic spectrum, power loss and temperature rise in individual filter components is predicted analytically and verified by actual tests using a 3 phase 10 kW grid connected converter setup. Experimental results of filtering characteristics show a good match with analysis in the frequency range of interconnected inverter applications. The design process is stream-lined for the above specified core and winding types. The output harmonic current spectrum is sampled and it is established that the harmonics are within the IEEE recommended limits. The analytical equations predicting the power loss and temperature rise are verified by experimental results. Based on the findings, new LCL filter combinations are formulated by varying the net Lpu to achieve the highest efficiency while still meeting the recommended IEEE specifications. Thus a design procedure which can enable an engineer to design the most efficient and compact filter that can also meet the recommended guidelines of harmonic filtering for grid-connected converter applications is established.

A review of recent literature and the research effort underlying this dissertation indicates that fractional order differential equations have significant potential to advance dynamical system methods broadly. Particular promise exists in the area of control and estimation, even for systems where fractional order models do not arise “naturally”. This dissertation is aimed at further building of the base methodology with a focus on robust feedback control and state estimation. By setting the mathematical foundation with the fractional derivative Caputo definition, we can expand the concept of the fractional order calculus in a way that enables us to build corresponding controllers and estimators in the state-space form. For the robust eigenstructure assignment, we first examine the conditioning problem of the closed-loop eigenvalues and stability robustnesss criteria for the fractional order system, and we find a unique application of an n-dimensional rotation algorithm developed by Mortari, to solve the robust eigenstructure assignment problem in a novel way. In contradistinction to the existing Fractional Kalman filter developed by using Gru ̈ndwald-Letnikov definition, the new Fractional Kalman filter that we establish by utilizing Caputo definition and our algorithms provide us with powerful means for solving practical state estimation problems for fractional order systems.

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 addressed using sequential Monte Carlo methods, commonly referred to as particle methods. When there is a linear sub-structure inherent in the underlying model, this can be exploited by the powerful combination of the particle filter and the Kalman filter, presented by the marginalized particle filter. This algorithm is also known as the Rao-Blackwellized particle filter and it is thoroughly derived and explained in conjunction with a rather general class of mixed linear/nonlinear state-space models. Models of this type are often used in studying positioning and target tracking applications. This is illustrated using several examples from the automotive and the aircraft industry. Furthermore, the computational complexity of the marginalized particle filter is analyzed. The parameter estimation problem is addressed for a relatively general class of mixed linear/nonlinear state-space models. The expectation maximization algorithm is used to calculate parameter estimates from batch data. In devising this algorithm, the need to solve a nonlinear smoothing problem arises, which is handled using a particle smoother. The use of the marginalized particle filter for recursive parameterestimation is also investigated. The applications considered are the camera positioning problem arising from augmented reality and sensor fusion problems originating from automotive active safety systems. The use of vision measurements in the estimation problem is central to both applications. In augmented reality, the estimates of the camera’s position and orientation are imperative in the process of overlaying computer generated objects onto the live video stream. The objective in the sensor fusion problems arising in automotive safety systems is to provide information about the host vehicle and its surroundings, such as the position of other vehicles and the road geometry. Information of this kind is crucial for many systems, such as adaptive cruise control, collision avoidance and lane guidance.

Aquesta tesi gira al voltant del disseny de receptors per a sistemes globals de navegació per satèl·lit (Global Navigation Satellite Systems, GNSS). El terme GNSS fa referència a tots aquells sistemes de navegació basats en una constel·lació de satèl·lits que emeten senyals de navegació útils per a posicionament. El més popular és l'americà GPS, emprat globalment. Els esforços d'Europa per a tenir un sistema similar veuran el seu fruit en un futur proper, el sistema s'anomena Galileo. Altres sistemes globals i regionals existeixen dissenyats per al mateix objectiu: calcular la posició dels receptors. Inicialment la tesi presenta l'estat de l'art en GNSS, a nivell de l'estructura dels actuals senyals de navegació i pel que fa a l'arquitectura dels receptors.El disseny d'un receptor per a GNSS consta d'un seguit de blocs funcionals. Començant per l'antena receptora fins al càlcul final de la posició del receptor, el disseny proporciona una gran motivació per a la recerca en diversos àmbits. Tot i que la cadena de Radiofreqüència del receptor també és comentada a la tesis, l'objectiu principal de la recerca realitzada recau en els algorismes de processament de senyal emprats un cop realitzada la digitalització del senyal rebut. En un receptor per a GNSS, aquests algorismes es poden dividir en dues classes: els de sincronisme i els de posicionament. Aquesta classificació correspon als dos grans processos que típicament realitza el receptor. Primer, s'estima la distancia relativa entre el receptor i el conjunt de satèl·lits visibles. Aquestes distancies es calculen estimant el retard patit pel senyal des de que és emès pel corresponent satèl·lit fins que és rebut pel receptor. De l'estimació i seguiment del retard se n'encarrega l'algorisme de sincronisme. Un cop calculades la distancies relatives als satèl·lits, multiplicant per la velocitat de la llum el retards estimats, l'algorisme de posicionament pot operar. El posicionament es realitza típicament pel procés de trilateralització: intersecció del conjunt d'esferes centrades als satèl·lits visibles i de radi les distancies estimades relatives al receptor GNSS. Així doncs, sincronització i posicionament es realitzen de forma seqüencial i ininterrompudament. La tesi fa contribucions a ambdues parts, com explicita el subtítol del document.Per una banda, la tesi investiga l'ús del filtrat Bayesià en el seguiment dels paràmetres de sincronisme (retards, desviaments Doppler i phases de portadora) del senyal rebut. Una de les fonts de degradació de la precisió en receptors GNSS és la presència de repliques del senyal directe, degudes a rebots en obstacles propers. És per això que els algorismes proposats en aquesta part de la tesi tenen com a objectiu la mitigació de l'efecte multicamí. La dissertació realitza una introducció dels fonaments teòrics del filtrat Bayesià, incloent un recull dels algorismes més populars. En particular, el Filtrat de Partícules (Particle Filter, PF) s'estudia com una de les alternatives més interessants actualment per a enfrontar-se a sistemes no-lineals i/o no-Gaussians. Els PF són mètodes basats en el mètode de Monte Carlo que realitzen una caracterització discreta de la funció de probabilitat a posteriori del sistema. Al contrari d'altres mètodes basats en simulacions, els PF tenen resultats de convergència que els fan especialment atractius en casos on la solució òptima no es pot trobar. En aquest sentit es proposa un PF que incorpora un seguit de característiques que el fan assolir millors prestacions i robustesa que altres algorismes, amb un nombre de partícules reduït. Per una banda, es fa un seguiment dels estats lineals del sistema mitjançant un Filtre de Kalman (KF), procediment conegut com a Rao-Blackwellization. Aquest fet provoca que la variància de les partícules decreixi i que un menor nombre d'elles siguin necessàries per a assolir una certa precisió en l'estimació de la distribució a posteriori. D'altra banda, un dels punts crítics en el disseny de PF és el disseny d'una funció d'importància (emprada per a generar les partícules) similar a l'òptima, que resulta ésser el posterior. Aquesta funció òptima no està disponible en general. En aquesta tesi, es proposa una aproximació de la funció d'importància òptima basada en el mètode de Laplace. Paral·lelament es proposen algorismes com l'Extended Kalman Filter (EKF) i l'Unscented Kalman Filter (UKF), comparant-los amb el PF proposat mitjançant simulacions numèriques.Per altra banda, la presentació d'un nou enfocament al problema del posicionament és una de les aportacions originals de la tesi. Si habitualment els receptors operen en dos passos (sincronització i posicionament), la proposta de la tesi rau en l'Estimació Directa de la Posició (Direct Position Estimation, DPE) a partir del senyal digital. Tenint en compte la novetat del mètode, es proporcionen motivacions qualitatives i quantitatives per a l'ús de DPE enfront al mètode convencional de posicionament. Se n'ha estudiat l'estimador de màxima versemblança (Maximum Likelihood, ML) i un algorisme per a la seva implementació pràctica basat en l'algorisme Accelerated Random Search (ARS). Els resultats de les simulacions numèriques mostren la robustesa de DPE a escenaris on el mètode convencional es veu degradat, com per exemple el cas d'escenaris rics en multicamí. Una de les reflexions fruit dels resultats és que l'ús conjunt dels senyals provinents dels satèl·lits visibles proporciona millores en l'estimació de la posició, doncs cada senyal està afectada per un canal de propagació independent. La tesi també presenta l'extensió de DPE dins el marc Bayesià: Bayesian DPE (BDPE). BDPE manté la filosofia de DPE, tot incloent-hi possibles fonts d'informació a priori referents al moviment del receptor. Es comenten algunes de les opcions com l'ús de sistemes de navegació inercials o la inclusió d'informació atmosfèrica. Tot i així, cal tenir en compte que la llista només està limitada per la imaginació i l'aplicació concreta on el marc BDPE s'implementi.Finalment, la tesi els límits teòrics en la precisió dels receptors GNSS. Alguns d'aquests límits teòrics eren ja coneguts, d'altres veuen ara la llum. El límit de Cramér-Rao (Cramér-Rao Bound, CRB) ens prediu la mínima variància que es pot obtenir en estimar un paràmetre mitjançant un estimador no esbiaixat. La tesi recorda el CRB dels paràmetres de sincronisme, resultat ja conegut. Una de les aportacions és la derivació del CRB de l'estimador de la posició pel cas convencional i seguint la metodologia DPE. Aquests resultats proporcionen una comparativa asimptòtica dels dos procediments pel posicionament de receptors GNSS. D'aquesta manera, el CRB de sincronisme pel cas Bayesià (Posterior Cramér-Rao Bound, PCRB) es presenta, com a límit teòric dels filtres Bayesians proposats en la tesi. / This dissertation deals with the design of satellite-based 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 state-of-the-art 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 (time-delays, Doppler-shifts and carrier-phases) 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 line-of-sight 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 simulation-based algorithms, based on Monte-Carlo methods. PF provide a discrete characterization of the posterior distribution of the system. Conversely to other simulation-based 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 Rao-Blackwellization. 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 two-steps 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 two-steps 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 multipath-rich 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 Cramér-Rao 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 ofthe 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 Cramér-Rao Bound, PCRB) is given, used as a fundamental limit of the Bayesian filters proposed in the thesis.

The Vehicular Ad-hoc Network (VANET) has been studied in many fields since it has the ability to provide a variety of services, such as detecting oncoming collisions and providing warning signals to alert the driver. The services provided by VANET are often based on collaboration among vehicles that are equipped with relatively simple motion sensors and GPS units. Awareness of its precise location is vital to every vehicle in VANET so that it can provide accurate data to its peers. Currently, typical localization techniques integrate GPS receiver data and measurements of the vehicle’s motion. However, when the vehicle passes through an environment that creates a multipath effect, these techniques fail to produce the high localization accuracy that they attain in open environments. Unfortunately, vehicles often travel in environments that cause a multipath effect, such as areas with high buildings, trees, or tunnels. The goal of this research is to minimize the multipath effect with respect to the localization accuracy of vehicles in VANET. The proposed technique first detects whether there is a noise in the vehicle location estimate that is caused by the multipath effect using neural network technique. It next takes advantage of the communications among the VANET vehicles in order to obtain more information from the vehicle’s neighbours, such as distances from target vehicle and their location estimates. The proposed technique integrates all these pieces of information with the vehicle’s own data and applies optimization techniques in order to minimize the location estimate error. The new techniques presented in this thesis decrease the error in the location estimate by 53% in the best cases, and in the worst case produce almost the same error in the location estimate as the traditional technique. Moreover, the simulation results show that 60% of the vehicles in VANET decrease the error in their location estimates by more than 13.8%.

Non-linear Bayesian estimation, or estimation of the state of a non-linear stochastic system from a set of indirect noisy measurements is a problem encountered in several fields of science. The particle filter and the ensemble Kalman filter are both used to get sub-optimal solutions of Bayesian inference problems, particularly for high-dimensional non-Gaussian and non-linear models. Both are essentially Monte Carlo techniques that compute their results using a set of estimated trajectories of the variable to be monitored. It has been shown that in a linear and Gaussian environment, solutions obtained from both these filters converge to the optimal solution obtained by the Kalman Filter. However, it is of interest to explore how the two filters compare to each other in basic methodology and construction, especially due to the similarity between them. In this work, we take up a specific problem of Bayesian inference in a restricted framework and compare analytically the results obtained from the particle filter and the ensemble Kalman filter. We show that for the chosen model, under certain assumptions, the two filters become methodologically analogous as the sample size goes to infinity.