Utilizing a Global Navigation Satellite System (GNSS) together with an Inertial Navigation System (INS) is today a common integration method to obtain a positioning solution for autonomous systems. Both GNSS and INS have benefits and weaknesses where the best parts from both systems can be combined with a Kalman filter. Because of this complementary nature, it is of interest to look at the robustness of the positioning solution when the Global Navigation Satellite System is temporarily not available. The aim of the thesis has been to investigate different vehicle models and to evaluate their short-time performance using a Dead Reckoning approach. The goal has been to develop a system for a Heavy Duty Vehicle (HDV) and to find out for which time interval a specific model can stay within a certain range when the GNSS is lost. A GNSS outage could for example happen when driving on a highway and passing signs, bridges and especially when driving inside tunnels. Also, for a solution to become commercially interesting, it must be cheap. Therefore, is it common to use so called Micro-Electro-Mechanical-Systems (MEMS) sensors which are of low-cost but suffer from biases, scale factors and temperature dependencies which must be compensated for. The results from the tests show that some models are able to estimate the position with good precision during short time GNSS outages whereas other models do not deliver the required accuracy. The main conclusion is that care should be taken when choosing the vehicle model so that it fits its usage area and the complexity needed to describe its motion. There are also lots of parameters to look at when investigating the best solution, where modeling of the low-cost sensors is one of them.

An existing drivetrain model with clutch temperature dynamics has been used to simulate the behaviour of a heavy duty truck. During the implementation of the model in MATLAB/Simulink modularity and simplicity was greatly emphasized. This was done in order to facilitate the use of the model in various applications as well as making it easy to understand. The main contributions of the thesis is however the adaptation of a clutch temperature and wear observer for use in an on-line application in the gearbox management system (GMS). The process of taking the observer from an off-line simulation environment to running on-line includes taking into consideration the configuration and limitations of the GMS as well as adapting the interface of the observer. Concretely this means dealing with the limitations of the available data types in the GMS, compensating for the effect of biased measurements as well as accounting for the different dynamics of the sensor-types used in the clutch.In a simulation environment the performance of the adapted observer has been studied and its ability to compensate for heat expansion and wear in the clutch shown. / En existerande drivlinemodell med temperaturdynamik i kopplingen har använts för att simulera beteendet hos en lastbil. Vid implementation av modellen i MATLAB/Simulink betonades vikten av en enkel och modulär struktur. Detta gjordes för att underlätta användning av modellen i olika applikationer samt för att göra den lätt att förstå.De huvudsakliga bidragen i uppsatsen är anpassningen av en temperatur- och slitageobeservatör på kopplingen för användning i realtid av växellådans styrenhet. För att ta observatören från simulerings- till realtidsmiljö måste styrenhetens konfiguration och begränsningar beaktas samt gränssnittet hos observatören anpassas. Konkret betyder detta att hänsyn till begränsningarna hos de olika datatyper som används i kopplingens styrenhet tagits, att den negativa inverkan som brusiga mätsignaler kan få begränsats samt att skillnader i dynamik hos de olika sensortyper som används i kopplingen kompenserats för. Med simuleringar har prestandan hos den anpassade observatören studerats samt dess förmåga att kompensera för värmeutvidgning och slitage i kopplingen visats.

In the last decade, Global Navigation Satellites Systems (GNSS) have gained a significant position in the development of urban navigation applications and associated services. The urban environment presents several challenges to GNSS signal reception, such as multipath and GNSS Line-of-Sight (LOS) blockage, which are translated in the positioning domain in a decreased navigation solution accuracy up to the lack of an available position. For this matter, Vector Tracking (VT) constitutes a promising approach able to cope with the urban environment-induced effects including multipath, NLOS reception and signal outages. This thesis is particularly focused on the proposal and design of a dual constellation GPS + Galileo single frequency L1/E1 Vector Delay Frequency Lock Loop (VDFLL) architecture for the automotive usage in urban environment. From the navigation point of view, VDFLL represents a concrete application of information fusion, since all the satellite tracking channels are jointly tracked and controlled by the common navigation Extended Kalman filter (EKF). The choice of the dual-constellation single frequency vector tracking architecture ensures an increased number of observations and at the same time allowing the conservation of the low-cost feasibility criteria of the mobile user’s receiver. Moreover, the use of single frequency L1 band signals implies the necessity of taking into account the ionospheric error effect. In fact, even after the application of the ionosphere error correction models, a resultant ionospheric residual error still remains in the received observations. The originality of this work relies on the implementation of a dual-constellation VDFLL architecture, capable of estimating the ionosphere residual error present in the received observations. This dissertation investigates the VDFLL superiority w.r.t the scalar tracking receiver in terms of positioning performance and tracking robustness for a real car trajectory in urban area in the presence of multipath and ionosphere residual error.

Durant la dernière décennie, les systèmes de navigation par satellites ont obtenu une place majeure dans le développement d’application de navigation urbaine et les services associés. L’environnement urbain pose plusieurs défis à la réception des signaux GNSS, comprenant les multi-trajets et le phénomène de blocage des signaux directs, qui peuvent se traduire dans le domaine de la position, par une diminution de la précision de la solution de navigation voire par une indisponibilité de la position. Dans cette situation, la poursuite vectorielle constitue une approche intéressante capable de contrecarrer les effets propres à un environnement urbain tels que les multi-trajets, les réceptions de signaux non directs et les interruptions de signal. Cette thèse s’intéresse particulièrement à la proposition et au design d’une architecture double constellation GPS + Galileo, mono-fréquence L1/E1 VDFLL pour les véhicules routiers en milieu urbain. Concernant la navigation, le VDFLL représente une application concrète de la fusion d’information dû au fait que tous les canaux de poursuite sont contrôlés par le même filtre de navigation sous la forme d’un Filtre de Kalman Étendu (EKF). Le choix de l’architecture double constellation mono-fréquence a pour but d’augmenter le nombre de mesures et garantit une faisabilité bas coût du récepteur mobile. De plus, l’utilisation des signaux de mono-fréquence L1 implique la prise en compte des perturbations causées par la ionosphère. Malgré l’application des modèles de corrections ionosphérique, un résidu d’erreur ionosphérique reste toujours présent. L’originalité de ce travail repose sur l’implémentation d’une architecture VDFLL double constellation capable d’estimer le résidu d’erreurs ionosphériques présent sur les observations reçues. Ce doctorat analyse les avantages apportés par la solution proposée par rapport à la poursuite scalaire au regard de performances de positionnement et de robustesse de poursuite dans le cadre d’une trajectoire de véhicule en milieu urbain et en présence de multi-trajets et de résidus d’erreur ionosphérique. / In the last decade, Global Navigation Satellites Systems (GNSS) have gained a significant position in the development of urban navigation applications and associated services. The urban environment presents several challenges to GNSS signal reception, such as multipath and GNSS Line-of-Sight (LOS) blockage, which are translated in the positioning domain in a decreased navigation solution accuracy up to the lack of an available position. For this matter, Vector Tracking (VT) constitutes a promising approach able to cope with the urban environment-induced effects including multipath, NLOS reception and signal outages. This thesis is particularly focused on the proposal and design of a dual constellation GPS + Galileo single frequency L1/E1 Vector Delay Frequency Lock Loop (VDFLL) architecture for the automotive usage in urban environment. From the navigation point of view, VDFLL represents a concrete application of information fusion, since all the satellite tracking channels are jointly tracked and controlled by the common navigation Extended Kalman filter (EKF). The choice of the dual-constellation single frequency vector tracking architecture ensures an increased number of observations and at the same time allowing the conservation of the low-cost feasibility criteria of the mobile user’s receiver. Moreover, the use of single frequency L1 band signals implies the necessity of taking into account the ionospheric error effect. In fact, even after the application of the ionosphere error correction models, a resultant ionospheric residual error still remains in the received observations. The originality of this work relies on the implementation of a dual-constellation VDFLL architecture, capable of estimating the ionosphere residual error present in the received observations. This dissertation investigates the VDFLL superiority w.r.t the scalar tracking receiver in terms of positioning performance and tracking robustness for a real car trajectory in urban area in the presence of multipath and ionosphere residual error.

The Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Ensemble Kalman Filter (EnKF) are commonly implemented practical solutions for solving nonlinear state space estimation problems; all based on the linear state space estimator, the Kalman Filter. Often, the UKF and EnKF are cited as a superior methods to the EKF with respect to error-based performance criteria. The UKF in turn has the advantage over the EnKF of smaller computational complexity. In practice however the UKF often fails to live up to this expectation, with performance which does not surpass the EKF and estimates which are not as robust as the EnKF. This work explores the geometry of alternative sigma point sets, which form the basis of the UKF, contributing several new sets along with novel methods used to generate them. In particular, completely novel systems of sigma points that preserve higher order statistical moments are found and evaluated. Additionally a new method for scaling and problem specific tuning of sigma point sets is introduced as well as a discussion of why this is necessary, and a new way of thinking about UKF systems in relation to the other two Kalman Filter methods. An Iterated UKF method is also introduced, similar to the smoothing iterates developed previously for the EKF. The performance of all of these methods is demonstrated using problem exemplars with the improvement of the contributed methods highlighted.

<p>Control and diagnosis of complex systems demand accurate knowledge of certain quantities to be able to control the system efficiently and also to detect small errors. Physical sensors are expensive and some quantities are hard or even impossible to measure with physical sensors. This has made model-based estimation an attractive alternative.</p><p>Model-based estimators are sensitive to errors in the model and since the model complexity needs to be kept low, the accuracy of the models becomes limited. Further, modeling is hard and time consuming and it is desirable to design robust estimators based on existing models. An experimental investigation shows that the model deficiencies in engine applications often are stationary errors while the dynamics of the engine is well described by the model equations. This together with fairly frequent appearance of sensor offsets have led to a demand for systematic ways of handling stationary errors, also called bias, in both models and sensors.</p><p>In the thesis systematic design methods for reducing bias in estimators are developed. The methods utilize a default model and measurement data. In the first method, a low order description of the model deficiencies is estimated from the default model and measurement data, resulting in an automatic model augmentation. The idea is then to use the augmented model for estimator design, yielding reduced stationary estimation errors compared to an estimator based on the default model. Three main results are: a characterization of possible model augmentations from observability perspectives, an analysis of what augmentations that are possible to estimate from measurement data, and a robustness analysis with respect to noise and model uncertainty.</p><p>An important step is how the bias is modeled, and two ways of describing the bias are introduced. The first is a random walk and the second is a parameterization of the bias. The latter can be viewed as an extension of the first and utilizes a parameterized function that describes the bias as a function of the operating point of the system. The parameters, rather than the bias, are now modeled as random walks, which eliminates the trade-off between noise suppression in the parameter convergence and rapid change of the offset in transients. This is achieved by storing information about the bias in different operating points. A direct application for the parameterized bias is the adaptation algorithms that are commonly used in engine control systems.</p><p>The methods are applied to measurement data from a heavy duty diesel engine. A first order model augmentation is found for a third order model and by modeling the bias as a random walk, an estimation error reduction of 50\,\% is achieved for a European transient cycle. By instead letting a parameterized function describe the bias, simulation results indicate similar, or better, improvements and increased robustness.</p>

<p>The object of this thesis is to provide a MATLAB framework for nonlinear filtering in general, and particle filtering in particular. This is done by using the object-oriented programming paradigm, resulting in truly expandable code. Three types of discrete and nonlinear state-space models are supported by default, as well as three filter algorithms: the Extended Kalman Filter and the SIS and SIR particle filters. Symbolic expressions are differentiated automatically, which allows for comfortable EKF filtering. A graphical user interface is also provided to make the process of filtering even more convenient. By implementing a specified interface, programming new classes for use within the framework is easy and guidelines for this are presented.</p>

<p>This thesis aims the problem of underwater vehicle positioning. The vehicle usedwas a Saab Seaeye Falcon which was equipped with a Doppler Velocity Log(DVL)manufactured by RD Instruments and an inertial measurement unit (IMU) fromXsense. During the work several different Extended Kalman Filter (EKF) havebeen tested both with a hydrodynamic model of the vehicle and a model withconstant acceleration and constant angular velocity. The filters were tested withdata from test runs in lake Vättern. The EKF with constant acceleration andconstant angular velocity appeared to be the better one. The misalignment of thesensors were also tried to be estimated but with poor result.</p>

<p>Starting from Maxwell’s equations, we derive a sensor model for three-axis magnetometerssuitable for localization and tracking applications. The model dependson the relative position between the sensor and the target, orientation of the targetand its magnetic signature. Both point targets and extended target modelsare provided. The models are validated on data taken from various road vehicles.The suitability of magnetometers for tracking is analyzed in terms of local observabilityand Cramér Rao lower bound as a function of the sensor positions in atwo sensor scenario. Also the signal to noise ratio is computed to determine theeffective range of the magnetometer. Results from field test data indicate excellenttracking of position and velocity of the target, as well as identification of themagnetic target model suitable for target classification.</p><p> </p>

<p>En patenterad metod [11] ligger till grund för utförandet av det här examensarbetet där målet var att beskriva metoden i reglertekniska termer. En lösning för att kompensera för gyrodrifter var också önskvärd. En olinjär modell som beskriver flygplanets dynamik har tagits fram och simulerats. Modellen har linjäriserats kring en trajektoria och diskretiserats för att undersöka observerbarheten för systemet. Huruvida ett system är observerbart eller inte avgör om det är möjligt eller inte att applicera ett extended kalmanfilter (EKF) på för att skatta systemets tillstånd. Kalmanfiltret används för att man inte har tillgång till mätningar på systemets alla tillstånd och måste därför skatta dem. Olika tester har gjorts för att undersöka kalmanfiltrets prestanda. Trots att kalmanfiltret är ett optimalt filter kan man inte garantera tillfredsställande resultat eftersom antalet tillgängliga mätbara tillstånd påverkar hur bra skattningar man får.</p>