Robot localization is one of the most important subjects in the Robotics science. It is an interesting and complicated topic. There are many algorithms to solve the problem of localization. Each localization system has its own set of features, and based on them, a solution will be chosen. In my thesis, I want to present a solution to find the best estimate for a robot position in certain space for which a map is available. The thesis started with an elementary introduction to the probability and the Gaussian theories. Simple and advanced practical examples are presented to illustrate each concept related to localization. Extended Kalman Filter is chosen to be the main algorithm to find the best estimate of the robot position. It was presented through two chapters with many examples. All these examples were simulated in Matlab in this thesis in order to give the readers and future students a clear and complete introduction to Kalman Filter. Fortunately, I applied this algorithm on a robot that I have built its base from scratch. MCECS-Bot was a project started in Winter 2012 and it was assigned to me from my adviser, Dr. Marek Perkowski. This robot consists of the base with four Mecanum wheels, the waist based on four linear actuators, an arm, neck and head. The base is equipped with many sensors, which are bumper switches, encoders, sonars, LRF and Kinect. Additional devices can provide extra information as backup sensors, which are a tablet and a camera. The ultimate goal of this thesis is to have the MCECS-Bot as an open source system accessed by many future classes, capstone projects and graduate thesis students for education purposes. A well-known MRPT software system was used to present the results of the Extended Kalman Filter (EKF). These results are simply the robot positions estimated by EKF. They are demonstrated on the base floor of the FAB building of PSU. In parallel, simulated results to all different solutions derived in this thesis are presented using Matlab. A future students will have a ready platform and a good start to continue developing this system.

Precise velocity and acceleration information is required for many real time applications. A standalone GPS receiver can be used to derive such information; however, there are many unsolved problems in this regard. This thesis establishes the theoretical basis for precise velocity and acceleration determination using a standalone GPS receiver in real time. An intensive investigation has been conducted into the Doppler effect in GPS. A highly accurate Doppler shift one-way observation equation is developed based on a comprehensive error analysis of each contributing factor including relativistic effects. Various error mitigation/elimination methods have been developed to improve the measurement accuracy of both the Doppler and Doppler-rate. Algorithms and formulae are presented to obtain real-time satellite velocity and acceleration in the ECEF system from the broadcast ephemeris. Low order IIR differentiators are designed to derive Doppler and Doppler-rate measurements from the raw GPS data for real-time applications. Abnormalities and their corresponding treatments in real-time operations are also discussed. In addition to the velocity and acceleration determination, this thesis offers a good tool for GPS measurement modelling and for design of interpolators, differentiators, as well as Kalman filters. The relativistic terms presented by this thesis suggest that it is possible to measure the geopotential directly using Doppler shift measurements. This may lead to a foundation for the development of a next generation satellite system for geodesy in the future.

<p>This master thesis is a study on how a rategyro triad, an accelerometer triad, and a magnetometer triad can be integrated into a navigation system, estimating a vehicle’s attitude, i.e. its roll, tipp, and heading angles. When only a rategyro triad is used to estimate a vehicle’s attitude, a drift in the attitude occurs due to sensor errors. </p><p>When an accelerometer triad and a magnetometer triad are used, an error in the vehicle’s heading, appearing as a sine curve, depending on the heading, occurs. By integrating these sensor triads, the sensor errors have been estimated with a filter to improve the estimated attitude’s accuracy. </p><p>To investigate and evaluate the navigation system, a simulation model has been developed in Simulink/Matlab. The implementation has been made using a Kalman filter where the sensor fusion takes place. Simulations for different scenarios have been made and the results from these simulations show that the drift in the vehicle’s attitude is avoided.</p>

<p>Automotive positioning systems relying exclusively on the input from a GPS receiver, which is a line of sight sensor, tend to be sensitive to situations with limited sky visibility. Such situations include: urban environments with tall buildings; inside parking structures; underneath trees; in tunnels and under bridges. In these situations, the system has to rely on integration of relative motion sensors to estimate vehicle position. However, these sensor measurements are generally affected by errors such as offsets and scale factors, that will cause the resulting position accuracy to deteriorate rapidly once GPS input is lost. </p><p>The approach in this thesis is to use a GPS receiver in combination with low cost sensor equipment to produce a robust positioning module. The module should be capable of handling situations where GPS input is corrupted or unavailable. The working principle is to calibrate the relative motion sensors when GPS is available to improve the accuracy during GPS intermission. To fuse the GPS information with the sensor outputs, different models have been proposed and evaluated on real data sets. These models tend to be nonlinear, and have therefore been processed in an Extended Kalman Filter structure. </p><p>Experiments show that the proposed solutions can compensate for most of the errors associated with the relative motion sensors, and that the resulting positioning accuracy is improved accordingly.</p>

<p>A face tracker is exactly what it sounds like. It tracks a face in a video sequence. Depending on the complexity of the tracker, it could track the face as a rigid object or as a complete deformable face model with face expressions.</p><p>This report is based on the work of a real time feature based face tracker. Feature based means that you track certain features in the face, like points with special characteristics. It might be a mouth or eye corner, but theoretically it could be any point. For this tracker, the latter is of interest. Its task is to extract global parameters, i.e. rotation and translation, as well as dynamic facial parameters (expressions) for each frame. It tracks feature points using motion between frames and a textured face model (Candide). It then uses an extended Kalman filter to estimate the parameters from the tracked feature points.</p>

by Kenneth M. Keverian, Nils R. Sandell, Jr. / Office of Naval Research Contract ONR/N00014-77-C-0532 (85552). / Originally presented as the first author's thesis, (B.S.) in the M.I.T. Dept. of Electrical Engineering and Computer Science, 1979. / Bibliography: p. 114-115.

Ph.D. / Electrical and Computer Engineering / Probabilistic inference is the problem of estimating the hidden variables (states or parameters) of a system in an optimal and consistent fashion as a set of noisy or incomplete observations of the system becomes available online. The optimal solution to this problem is given by the recursive Bayesian estimation algorithm which recursively updates the posterior density of the system state as new observations arrive. This posterior density constitutes the complete solution to the probabilistic inference problem, and allows us to calculate any "optimal" estimate of the state. Unfortunately, for most real-world problems, the optimal Bayesian recursion is intractable and approximate solutions must be used. Within the space of approximate solutions, the extended Kalman filter (EKF) has become one of the most widely used algorithms with applications in state, parameter and dual estimation. Unfortunately, the EKF is based on a sub-optimal implementation of the recursive Bayesian estimation framework applied to Gaussian random variables. This can seriously affect the accuracy or even lead to divergence of any inference system that is based on the EKF or that uses the EKF as a component part. Recently a number of related novel, more accurate and theoretically better motivated algorithmic alternatives to the EKF have surfaced in the literature, with specific application to state estimation for automatic control. We have extended these algorithms, all based on derivativeless deterministic sampling based approximations of the relevant Gaussian statistics, to a family of algorithms called Sigma-Point Kalman Filters (SPKF). Furthermore, we successfully expanded the use of this group of algorithms (SPKFs) within the general field of probabilistic inference and machine learning, both as stand-alone filters and as subcomponents of more powerful sequential Monte Carlo methods (particle filters). We have consistently shown that there are large performance benefits to be gained by applying Sigma-Point Kalman filters to areas where EKFs have been used as the de facto standard in the past, as well as in new areas where the use of the EKF is impossible.

Economic systems are often subject to structural variability. For the achievement of correct structural specification in econometric modelling it is then important to allow for parameters that are time-varying, and to apply estimation techniques suitably designed for inference in such models. One realistic model assumption for such parameter variability is the Markovian model, and Kaiman filtering is then assumed to be a convenient estimator. In the thesis several aspects of using Kaiman filtering approaches to estimation in that framework are considered. The application of the Kaiman filter to estimation in econometric models is straightforward if a set of basic assumptions are satisfied, and if necessary initial specifications can be accurately made. Typically, however, these requirements can generally not be perfectly met. It is therefore of great importance to know the consequences of deviations from the basic assumptions and correct initial specifications for inference, in particular for the small sample situations typical in econometrics. If the consequences are severe it is essential to develop techniques to cope with such aspects.For estimation in interdependent systems a two stage Kaiman filter is proposed and evaluated, theoretically, as well as by a small sample Monte Carlo study, and empirically. The estimator is approximative, but with promising small sample properties. Only if the transition matrix of the parameter model and an initial parameter vector are misspecified, the performance deteriorates. Furthermore, the approach provides useful information about structural properties, and forms a basis for good short term forecasting.In a reduced form fraaework most of the basic assumptions of the traditional Kaiman filter are relaxed, and the implications are studied. The case of stochastic regressors is, under reasonable additional assumptions, shown to result in an estimator structurally similar to that due to the basic assumptions. The robustness properties are such that in particular the transition matrix and the initial parameter vector should be carefully estimated. An estimator for the joint estimation of the transition matrix, the parameter vector and the model residual variance is suggested and utilized to study the consequences of a misspecified parameter model. By estimating th transitions the parameter estimates are seen to be robust in this respect. / <p>Härtill 4 delar</p> / digitalisering@umu

La utilización de filtros activos paralelo mejora la eficiencia del suministro eléctrico mediante la modificación de las características de la forma de onda de las corrientes de línea. Trabajos de investigación anteriores destacan la complicada estructura de los controladores empleados en este tipo de soluciones y su sensibilidad a la distorsión de la onda de tensión en el punto donde se realiza su conexión. El objetivo fundamental de esta tesis es el desarrollo de un controlador para filtros activos de potencia tolerante a desequilibrios de tensión, huecos de tensión y armónicos de tensión. Objetivo secundario de este trabajo es el diseño de los algoritmos necesarios para el control de un filtro activo de potencia paralelo polifásico mediante una tarjeta basada en un procesador digital de señal. / Shunt active power filters (APF) modify the phase current waveform characteristics, which allow to increase the efficiency of electrical power grids. Previous research works establish the complex structure of controllers applied to APFs and their sensibility to voltage waveform disturbances.The aim of this thesis is the developing of a controller for shunt active power filters with tolerance to voltage unbalances, voltage dips and voltage harmonics. The proposed controller, implemented on a DSP target board, will be tested on a three-phase active power filter.

The electrocardiogram (ECG) is a time-varying electrical signal that interprets the electrical activity of the heart. It is obtained by a non-invasive technique known as surface electromyography (EMG), used widely in hospitals. There are many clinical contexts in which ECGs are used, such as medical diagnosis, physiological therapy and arrhythmia monitoring. In medical diagnosis, medical conditions are interpreted by examining information and features in ECGs. Physiological therapy involves the control of some aspect of the physiological effort of a patient, such as the use of a pacemaker to regulate the beating of the heart. Moreover, arrhythmia monitoring involves observing and detecting life-threatening conditions, such as myocardial infarction or heart attacks, in a patient. ECG signals are usually corrupted with various types of unwanted interference such as muscle artifacts, electrode artifacts, power line noise and respiration interference, and are distorted in such a way that it can be difficult to perform medical diagnosis, physiological therapy or arrhythmia monitoring. Consequently signal processing on ECGs is required to remove noise and interference signals for successful clinical applications. Existing signal processing techniques can remove some of the noise in an ECG signal, but are typically inadequate for extraction of the weak ECG components contaminated with background noise and for retention of various subtle features in the ECG. For example, the noise from the EMG usually overlaps the fundamental ECG cardiac components in the frequency domain, in the range of 0.01 Hz to 100 Hz. Simple filters are inadequate to remove noise which overlaps with ECG cardiac components. Sameni et al. have proposed a Bayesian filtering framework to resolve these problems, and this gives results which are clearly superior to the results obtained from application of conventional signal processing methods to ECG. However, a drawback of this Bayesian filtering framework is that it must run offline, and this of course is not desirable for clinical applications such as arrhythmia monitoring and physiological therapy, both of which re- quire online operation in near real-time. To resolve this problem, in this thesis we propose a dynamical model which permits the Bayesian filtering framework to function online. The framework with the proposed dynamical model has less than 4% loss in performance compared to the previous (offline) version of the framework. The proposed dynamical model is based on theory from fixed-lag smoothing.