Dissertation (PhD)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

Thesis (MSc)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

Thesis (M.S.) University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (June 26, 2007) Includes bibliographical references.

Bernard C. Levy. / Bibliography: leaves 16-17. / "March 1984" / "ECS-83-12921" "AFOSR-82-0135A"

Ahmed H. Tewfik, Bernard C. Levy, Alan S. Willsky. / Bibliography: leaf 21. / "March 1985." / "...supported in part by the National Science Foundation under Grant ECS-83-12921" "...supported... in part by the Army Research Office under Grant No. DAAG-29-84-K-0005."

Common trends in the vehicle industry are semiautonomous functions and autonomous solutions. This new type of functionality sets high requirements on the knowledge about the state of the vehicle. A precise vehicle speed is important for many functions, and one example is the positioning system which often is reliant on an accurate speed estimation. This thesis investigates how an IMU (Inertial Measurement Unit), consisting of a gyroscope and an accelerometer, can support the vehicle speed estimation from wheel speed sensors. The IMU was for this purpose mounted on a wheelloader. To investigate the speed estimation EKFs (Extended Kalman Filters) with different vehicle and sensor models are implemented. Furthermore all filters are extended to Kalman smoothers. First an analysis of the sensors was performed. The EKFs were then developed and verified using a simulation model developed by Volvo Construction Equipment. The filters were also implemented on the wheel loader and tested on data collected from real world scenarios.

Este seminario tiene como objetivo resaltar la relevancia que tiene la modelación de la Curva de Rendimientos o Yield Curve, dada su extensa aplicación en los mercados financieros, especialmente en la valoración de activos. Para ello, se muestra y describe, bajo el contexto del Mercado de Renta Fija en Chile, el modelo dinámico descrito por Cox-Ingersoll y Ross en el que se utilizan estimaciones por modelos estado-espacio y Filtro de Kalman. Se resalta dicho modelo dada las ventajas que presenta frente a otros modelos descritos con respecto a la inclusión de no negatividad de la tasa y en el supuesto que la volatilidad no es constante, consiguiendo así una modelación más realista. De esta manera se pretende que dicho marco teórico sea fuente para próximas aplicaciones.

O presente trabalho consiste em um estudo dos processos fracionários generalizados via sistema de espaço de estados. Essa representação permitirá utilizar um procedimento recursivo para a estimação dos parâmetros desses processos chamado filtro de Kalman. Além disso, apresenta um estudo detalhado sobre o filtro de Kalman, investigando sua utilização na estimação de um processo k-Factor GARMA(p;u; λ; q). Este trabalho verifica que é possível representar um processo k-Factor GARMA(p;u; λ; q) causal por um sistema de espaço de estados, o que possibilita calcular recursivamente a função de verossimilhança exata do processo, por meio do uso do filtro de Kalman, mesmo que a sua representação em espaço de estados tenha dimensão infinita. / This paper presents a study of the generalized fractional processes by using the state space. This representation will allow to use a recursive procedure for the estimation of the parameters of these processes called Kalman filter. In addition, it presents a study with details about the Kalman filter, investigating their use in the estimation of a process k-Factor GARMA(p;u;λ ; q). This work find that is possible to represent a process k-Factor GARMA (p;u;λ ; q) causal in a state space system, which enable to compute recursively the exact likelihood function of the process through the use of the Kalman filter, even though their representation in state space has infinite dimension.

Mobile devices with wireless networking capabilities are used in a wide range of environments. Geolocation information increases the value of the data generated by a device and is vital in the development of a wide range of applications from autonomous vehicles to the Internet of things. Systems that generate signals specifically for geolocation have become widely adopted but, due to fundamental constraints, lack coverage and accuracy in complex urban and indoor environments. In addition to this, the reliance on a single signal source is not desirable in many applications that value the integrity of the geolocation estimate. A direction of research aiming to improve geolocation in indoor and urban environments measures signals of opportunity in order to generate a more robust estimate. While this approach improves signal availability, the unpredictable nature of these variable and uncontrolled signals leads to poor geolocation estimates, which are typically not suitable for use in many applications. This project aims to improve on the accuracy, resilience and integrity of a geolocation estimate obtained from signal of opportunity measurements in indoor and urban environments while reducing hardware requirements. This has been achieved by efficiently coupling signals of opportunity within the radio environment with other system signals, such as those from an inertial measurement unit. Research has been carried out to optimise the coupling of these data sources resulting in techniques to allow the identification and removal of key error drivers from both the radio environment and other system sensors. This thesis proposes a specifically designed extended Kalman filter to improve on the signal coupling. The filter aims to optimise the accuracy of radio environment measurements while also providing the ability to identify signal error sources in urban and indoor environments, leading to both greater accuracy and resilience of the geo-location estimate. Further, the proposed extended Kalman filter may use the radio environment as a source of geolocation data. The ability of the filter to recognise and mitigate leading radio environment error sources such as multipath and interference allowed the design of filters to obtain detailed and accurate signal strength and time of arrival information. The thesis also presents a thorough set of simulation and modelling experiments to investigate and optimise the efficiency of the proposed solutions in a range of environments. Validation testing confirmed that in the urban and indoor environments, the average error of geo-location estimates has been reduced from 10 m to 3 m without improvement to the hardware surrounding infrastructure. The improvements presented in this thesis allow networked devices to improve the value of their data by incorporating the context that comes from increased geolocation accuracy and resilience. In turn, this allows the development of a wide range of new location based applications for mobile devises in indoor and urban environments.

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica. / Made available in DSpace on 2013-06-25T22:28:40Z (GMT). No. of bitstreams: 1 313502.pdf: 2548956 bytes, checksum: 09e3d7288119541a1642c771da8cd879 (MD5) / Um dos problemas que envolvem as soluções para a mobilidade de robôs móveis terrestres é estimar a posição do robô com precisão juntamente com a exploração do ambiente, mapeando-o corretamente (SLAM - Simultaneous Localization and Mapping - Localização e Mapeamento Simultâneo). Embora vários algoritmos tenham sido desenvolvidos nos últimos anos, exigindo uma carga de cálculo computacional cada vez maior dos robôs,, estes estão susceptíveis a um mau desempenho quando os sensores apresentam ruídos, quando há problemas nos atuadores, variáveis não modeladas ou em virtude de algum imprevisto momentâneo no ambiente. A proposta deste trabalho é programar um SLAM para robôs móveis interligando-o a uma combinação de sensores inerciais com sensores de odometria através de uma técnica de fusão de sensores conhecida como filtro de Kalman Estendido, para reduzir a incerteza na estimação da posição e melhorar o desempenho do SLAM. Por consequência, o custo computacional é reduzido. O trabalho foi estruturado iniciando por uma revisão a respeito dos conceitos básicos de sensoriamento, a fim de contextualizar o problema e apresentar as nomenclaturas e termos utilizados. A seguir foram abordadas as técnicas de fusão de dados, as representações do robô e do ambiente, as técnicas de mapeamento e exploração e as diversas técnicas de navegação que podem ser utilizadas, para ambientes conhecidos epara ambientes desconhecidos. Essas informações são importantes para um melhor entendimento do problema, de como representá-lo e de como se pode avaliar os resultados obtidos. Na sequência é apresentado o SLAM, destacando as principais técnicas e em detalhes o Grid Based FastSLAM. É demonstrado através de simulações que quanto maior as incertezas sobre a posição do robô, um número maior de partículas é necessário para manter a qualidade do mapa gerado, e como cada partícula possui um mapa associado a si, o custo computacional é consideravelmente aumentado. Outro aspecto analisado foi o impacto na escolha da covariância associada à transição de estados, propondo a utilização da covariância inerente ao cálculo da fusão de sensores como parâmetro de refinamento no SLAM.<br> / Abstract : One of the problems in solutions involving land mobile robots is the estimation of the robot position with precision and at the same time, explore the environment and mapping it correctly (SLAM - Simultaneous Localization and Mapping ). Several algorithms were developed in the last years, demanding large computational resources in robots and even so, these may have a bad performance in cases of sensors having noises, problems in actuators, not modeled variables or when there is something in the environment that wasn't expected. This dissertation proposal is to program a SLAM algorithm for mobile robots and connect it with a sensor data fusion, between odometry and inertial sensors, using the Extended Kalman Filter, achieving a reduction of the position uncertainty and improving the SLAM performance, also reducing the need of computational resources. This work begins with a revision of concepts of robot sensors, needed to understand later algorithms and nomenclatures. In the following items it is described the sensor fusion techniques, the robot localization problem, the map and robot representation alternatives, and the navigation problems for explored and non-explored environments. These information are important for a better understanding of the problem, on how represent it and how to evaluate the obtained results. After this introduction, it's described some SLAM algorithms, featuring in details the Grid Based FastSLAM. It's demonstrated by simulations that as high uncertainty about robot position, as large are the number of particles needed to maintain the generated map quality. This implies in a large computational cost, thus improving the uncertainty with sensor data fusion makes the robot work with less particles. It is also showed that choosing the right covariance in robot transition model is very important and finding a way to connect the covariance of sensor data fusion with SLAM can improve performance even more.