The diploma work concerns on an issue of data collection of speed and electrical angle based on informations from Hall sensor with the necessary resolution for control of BLDC motor. Specifically, concenred on a section with low speed. Most of moors use Hall sensor for detecting speed and position of the rotor. At low speed section, becomes the situation when we can not determine the position of the rotor with (the) required (sufficient) resolution, this situation creates a „wince“ in the control (ripple torque). The task is to design and evaluate the possibilities of the algorithm for control and acquisition speed and rotor position with the required accuracy.

This thesis deals with a design of 9DOF module for the evaluation of orientation in space. The thesis includes a theoretical description of sensors, mathematical apparatus for rotating and the Kalman filter. The practical part deals with the design of hardware, firmware and software, whereby the mathematical formulas set out in the theoretical part are applied. In the last part a testing of the created module is presented.

The aim of this term project was to analyze wireless indoor localization. It contains analysis of some wireless localization techniques such as Time of Arrival or Time Difference of Arrival. The paper also describes the system of SEWIO Company. Main part of the master’s thesis is description, design and implementation of the Kalman filter. The Kalman filter is used to improve two-dimensional positional data and synchronization of anchors (devices for finding a position of an object in SEWIO system). There are described a few system models for the Kalman filter.

This diploma thesis deals with posibilities of tracking object movement using computer vision algorithms. First chapters contain review of methods used for background subtraction, there are also listed basic detection approaches and thesis also mentions algorithms which allows tracking and movement prediction. Next part of this work informs about algoritms implemented in resulting software and its graphical user interface. Evaluation and comparison of original and modified algorithms is stationed at the end of this text.

The growth in global population inevitably increases the consumption of natural resources. The need to provide basic services to these growing communities leads to an increase in anthropogenic changes to the natural environment. The resulting transformation of vegetation cover (e.g. deforestation, agricultural expansion, urbanisation) has significant impacts on hydrology, biodiversity, ecosystems and climate. Human settlement expansion is the most common driver of land cover change in South Africa, and is currently mapped on an irregular, ad hoc basis using visual interpretation of aerial photographs or satellite images. This thesis proposes several methods of detecting newly formed human settlements using hyper-temporal, multi-spectral, medium spatial resolution MODIS land surface reflectance satellite imagery. The hyper-temporal images are used to extract time series, which are analysed in an automated fashion using machine learning methods. A post-classification change detection framework was developed to analyse the time series using several feature extraction methods and classifiers. Two novel hyper-temporal feature extraction methods are proposed to characterise the seasonal pattern in the time series. The first feature extraction method extracts Seasonal Fourier features that exploits the difference in temporal spectra inherent to land cover classes. The second feature extraction method extracts state-space vectors derived using an extended Kalman filter. The extended Kalman filter is optimised using a novel criterion which exploits the information inherent in the spatio-temporal domain. The post-classification change detection framework was evaluated on different classifiers; both supervised and unsupervised methods were explored. A change detection accuracy of above 85% with false alarm rate below 10% was attained. The best performing methods were then applied at a provincial scale in the Gauteng and Limpopo provinces to produce regional change maps, indicating settlement expansion. / Thesis (PhD(Eng))--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted

One of the most challenging tasks in the oil industry is the production of reliable reservoir forecast models. Because of different sources of uncertainties the numerical models employed are often only crude approximations of the reality. This problem is tackled by the conditioning of the model with production data through data assimilation. This process is known in the oil industry as history matching. Several recent advances are being used to improve history matching reliability, notably the use of time-lapse seismic data and automated history matching software tools. One of the most promising data assimilation techniques employed in the oil industry is the ensemble Kalman filter (EnKF) because its ability to deal with highly non-linear models, low computational cost and easy computational implementation when compared with other methods. A synthetic reservoir model was used in a history matching study designed to predict the peak production allowing decision makers to properly plan field development actions. If only production data is assimilated, a total of 12 years of historical data is required to properly characterize the production uncertainty and consequently the correct moment to take actions and decommission the field. However if time-lapse seismic data is available this conclusion can be reached 4 years in advance due to the additional fluid displacement information obtained with the seismic data. Production data provides geographically sparse data in contrast with seismic data which are sparse in time. Several types of seismic attributes were tested in this study. Poisson’s ratio proved to be the most sensitive attribute to fluid displacement. In practical applications, however the use of this attribute is usually avoided due to poor quality of the data. Seismic impedance tends to be more reliable. Finally, a new conceptual idea was proposed to obtain time-lapse information for a history matching study. The use of crosswell time-lapse seismic tomography to map velocities in the interwell region was demonstrated as a potential tool to ensure survey reproducibility and low acquisition cost when compared with full scale surface surveys. This approach relies on the higher velocity sensitivity to fluid displacement at higher frequencies. The velocity effects were modeled using the Biot velocity model. This method provided promising results leading to similar RRMS error reductions when compared with conventional history matched surface seismic data.

This thesis investigates the concept of controlling a CubeSat constellation in low-Earth orbit. Low-Earth orbits are considered because the torque used for satellite control is supplied with magnetorquers, and the closer the satellite is to Earth’s magnetic field the more control gain can be supplied. Also, this is the expected orbit altitude of future CubeSat constellations to enable communications. Controlling a CubeSat relies on attitude determination. This means being able to estimate its attitude relative to a given reference frame. To determine the attitude, we propose to use a star tracker and a Kalman filter. A star tracker scans the stars in the satellite’s view, correlates the object to a database, to return an attitude measurement. The measurement is then processed using the Kalman filter. The attitude estimate is then used as the reference input for the controller. Once the attitude of the satellites is determined, a controller can be implemented; assuming the system is controllable and observable. These parameters are verified by adding enough actuators and sensors, respectively. The novelty of this thesis is constructing a controller that will take three satellites and their attitude estimates and arrange them broadside to a target. For simplicity, the arrangement will be a linear formation, and the target and satellite constellation will all be near-field communication. The goal is to place the satellite constellation in an attitude for an intersatellite link to be established. This is a proposed solution to better budget power and computational constraints associated with CubeSats. In addition to adjusting the topology of the system, a communication method must be considered for the data to be distributed across the system requiring an antenna design to implement the communication method. Both issues are discussed in the thesis; however, the focus is the controller design for attitude control. The control approach is a multi-input multi-output (MIMO) sliding fuzzy controller. The focus of the analysis is attitude control for communication while maintaining the constellation in a linear formation. The results shown this controller to be a valid proof of concept.

Die Arbeit befasst sich mit der Erkennung und Verfolgung von Straßenrandern in unstrukturierten Gebieten. Ein Lasermesssystem detektiert Punkte auf den Straßenr ändern. Mit Hilfe eines Kalman Filters wird der Straßenverlauf geschätzt. Dabei kommt ein vereinfachtes Straßenmodell zum Einsatz.

Eine Voraussetzung um einen Flugregler für Flugroboter zu realisieren, ist die Wahrnehmung der Bewegungen dieses Roboters. Diese Arbeit beschreibt einen Ansatz zur Schätzung der Bewegung eines autonomen Flugroboters unter Verwendung relativ einfacher, leichter und kostengünstiger Sensoren. Mittels eines Erweiterten Kalman Filters werden Beschleunigungssensoren, Gyroskope, ein Ultraschallsensor, sowie ein Sensor zu Messung des optischen Flusses zu einer robusten Bewegungsschätzung kombiniert. Dabei wurden die einzelnen Sensoren hinsichtlich der Eigenschaften experimentell untersucht, welche für die anschließende Erstellung des Filters relevant sind. Am Ende werden die Resultate des Filters mit den Ergebnissen einer Simulation und eines externen Tracking-Systems verglichen.

The necessity of accurate localization in mobile robotics is obvious - if a robot does not know where it is, it cannot navigate accurately to reach goal locations. Robots learn about their environment via sensors. Small robots require small, efficient, and, if they are to be deployed in large numbers, inexpensive sensors. The sensors used by robots to perceive the world are inherently inaccurate, providing noisy, erroneous data or even no data at all. Combined with estimation error due to imperfect modeling of the robot, there are many obstacles to successfully localizing in the world. Sensor fusion is used to overcome these difficulties - combining the available sensor data in order to derive a more accurate pose estimation for the robot. In this thesis, we dissect and analyze a wide variety of sensor fusion algorithms, with the goal of using a set of inexpensive sensors in a suite to provide real-time localization for a robot given unknown sensor errors and malfunctions. The sensor fusion algorithms will fuse GPS, INS, compass and control inputs into a more accurate position. The filters discussed include a SPKF-PF (Sigma-Point Kalman Filter - Particle Filter), a MHSPKF (Multi-hypothesis Sigma-Point Kalman Filter), a FSPKF (Fuzzy Sigma-Point Kalman Filter), a DFSPKF (Double Fuzzy Sigma-Point Kalman Filter), an EKF (Extended Kalman Filter), a MHEKF (Multi-hypothesis Extended Kalman Filter), a FEKF (Fuzzy Extended Kalman Filter), and a standard SIS PF (Sequential Importance Sampling Particle Filter). Our goal in this thesis is to provide a toolbox of algorithms for a researcher, presented in a concise manner. I will also simultaneously provide a solution to a difficult sensor fusion problem - an algorithm that is of low computational complexity (< O(n³)), real-time, accurate (equal in or more accurate than a DGPS (differential GPS) given lower quality sensors), and robust - able to provide a useful localization solution even when sensors are faulty or inaccurate. The goal is to find a locus between power requirements, computational complexity and chip requirements and accuracy/robustness that provides the best of breed for small robots with inaccurate sensors. While other fusion algorithms work well, the Sigma Point Kalman filter solves this problem best, providing accurate localization and fast response, while the Fuzzy EKF is a close second in the shorter sample with less error, and the Sigma-Point Kalman Particle Filter does very well in a longer example with more error. Fuzzy control is also discussed, especially the reason for its applicability and its use in sensor fusion.