The Kalman filter is an important component of optimal estimation theory. It has applications in a wide range of high performance control systems including navigational, fire control, and targeting systems. The Kalman filter, however, has not been utilized to its full potential due to the limitations of its inherent computational intensiveness which requires "off-line" processing or allows only low bandwidth real-time applications. The recent advances in VLSI circuit technology have created the opportunity to design algorithms and data structures for direct implementation in integrated circuits. A systolic architecture is a concept which allows the construction of massively parallel systems in integrated circuits and has been utilized as a means of achieving high data rates. A systolic system consists of a set of interconnected processing elements, each capable of performing some simple operation. The design of a processing element in an orthogonal systolic architecture will be investigated using the state of the art in VLSI technology. The goal is to create a high speed, high precision processing element which is adaptive to a highly configurable systolic architecture. In order to achieve the necessary high computational throughput, the arithmetic unit of the processing element will be implemented using the Logarithmic Number System. The Systolic architecture approach will be used in an attempt to implement a Kalman filtering system with both a high sampling rate and a small package size. The design of such a Kalman filter would enable this filtering technology to be applied to the areas of process control, computer vision, and robotics.

Position in the environment is essential in any autonomous system. As increased accuracy is required, the costs escalate accordingly. This paper presents a simple way to systematically integrate sensory data to provide a drivable and accurate position solution at a low cost. The data fusion is handled by a Kalman filter tracking five states and an undetermined number of asynchronous measurements. This implementation allows the user to define additional adjustments to improve the overall behavior of the filter. The filter is tested using a suite of inexpensive sensors and then compared to a differential GPS position. The output of the filter is indeed a drivable solution that tracks the reference position remarkably well. This approach takes advantage of the short-term accuracy of odometry measurements and the long-term fix of a GPS unit. A maximum error of two meters of deviation from the reference is shown for a complex path over two minutes and 100 meters long. / Master of Science

In the past, passive SONAR range tracking systems have used Extended Kalman filters to process nonlinear time-delay measurements. This approach has several flaws due to the inherent divergence problems of Extended Kalman filters. This paper discusses a new approach which uses a prefilter to linearize the measurements so that they can be processed by a standard Kalman filter. The approach is subsequently expanded for use with an adaptive Kalman filter which allows source maneuvers to be tracked. A new approach to passive Doppler velocity tracking is also proposed which uses a dedicated Kalman filter to track random fluctuations in the sources center frequency. This dedicated tracker simplifies the problem so that it can be handled by a basic adaptive Kalman filter. / Master of Science

No description available.

The method of Pseudo-Linear Identification (PLID) is developed for application in an adaptive control loop. The effects of noise are investigated for the case of full-order system identification, and the results are applied to the use of PLID as a reduced-order system estimator. A self-tuning regulator (STR) is constructed using PLID and the effects of reducing the expected order of the system are demonstrated. A second adaptive control algorithm is presented wherein the STR controller is varied to achieve some degree of closeness to a given model (model-reference adaptive control). / Master of Science

Titre de l'écran-titre (visionné le 23 mai 2023) / Les changements dans les propriétés du minerai apportent des défis pour le contrôle des broyeurs semi-autogènes (SAG) car ils sont généralement difficiles à mesurer en temps réel et ont des impacts significatifs sur le procédé. Bien qu'il y ait un manque de compréhension de la nature des variations des propriétés du minerai dans les opérations réelles, les données disponibles sur la distribution granulométrique indiquent qu'elles sont caractérisées par des changements abrupts et des comportements en rampe, pour lesquels les modèles de perturbation standard utilisés dans le contrôle des procédés ne sont pas conçus. Dans ce travail, un modèle de perturbation déterministe se produisant de manière aléatoire (randomly-occurring deterministic disturbances (RODDs)) est considéré. Celui-ci possède une entrée commutant entre deux bruits aléatoires. Puisque la perturbation n'est pas gaussienne, un filtre de Kalman standard, qui est généralement utilisé pour l'estimation d'état, n'est pas optimal. Les capacités de deux observateurs à modèles multiples de détecter et d'estimer les états de systèmes soumis à des RODD non mesurés sont évaluées. Ces observateurs maintiennent plusieurs estimations des états du système sur la base de différentes hypothèses sur la commutation de la perturbation. La vraisemblance de chaque hypothèse compte tenu des mesures disponibles est évaluée et utilisée pour produire une meilleure estimation des états et de la sortie du procédé, qui demeure toutefois sous-optimale. Deux types d'observateurs à modèles multiples sous-optimaux sont évalués et comparés à un filtre de Kalman standard en utilisant des mesures de bruit simulées à partir de trois systèmes différents--un système linéaire avec un RODD et une sortie, un système linéaire avec deux RODD et deux sorties, et une simulation réaliste d'un circuit de broyage avec une mesure de sortie et une alimentation commutant entre deux types de minerai. Les résultats montrent que les observateurs à modèles multiples détectent et réagissent rapidement aux changements instantanés de la perturbation, sans pour autant avoir une sensibilité accrue au bruit lorsqu'en régime permanent. Cela suggère que des modèles plus réalistes de perturbations du minerai alimenté et une meilleure estimation en temps réel des changements dans les propriétés du minerai pourraient améliorer le contrôle du procédé, bien que les gains par rapport à un filtre unique de Kalman dépendent du niveau du bruit de mesure. / Changes in ore properties create challenges for the control of semi-autogenous grinding (SAG) mills because they are generally difficult to measure in real time and have significant impacts on the grinding process. Although there is a lack of understanding of the nature of variations in ore properties in real operations, available data on the particle size distribution indicates that they are characterised by abrupt step changes and ramp behaviours, which standard disturbance models used in process control are not designed for. In this work, an alternative disturbance model known as the randomly-occurring deterministic disturbance (RODD) is considered. This has a switching random noise input, which makes it suitable for modelling these types of disturbances. However, since the noise is non-Gaussian, a standard Kalman filter, which is typically used for state estimation, is not optimal. The capabilities of two multiple-model observers to detect and estimate the states of systems subjected to unmeasured RODDs are evaluated. These observers maintain multiple estimates of the system states based on different hypotheses about the switching of the disturbance. The likelihood of each hypothesis given the available measurements is estimated and used to produce a better, although still sub-optimal, estimate the process states and output. Two types of sub-optimal multiple-model observer are evaluated and compared to a standard Kalman filter using simulated noisy measurements from three different process systems--a linear system with one RODD and one output, a linear system with two RODDs and two-outputs, and a realistic grinding process simulation with a switching ore feed and one output measurement. The results show that the multiple-model observers detect and respond quickly to step changes in the disturbance, without having a compromised sensitivity to noise during steady-state. This suggests that more realistic models of ore feed disturbances and improved real-time estimation of changes in ore properties could have benefits in terms of improved process control, although the improvement compared to a single Kalman filter was found to depend on the magnitude of the measurement noise.

A lane position system and enhancement techniques, for increasing the robustnessand availability of such a system, are investigated. The enhancements areperformed by using additional sensor sources like map data and GPS. The thesiscontains a description of the system, two models of the system and two implementedfilters for the system. The thesis also contains conclusions and results oftheoretical and experimental tests of the increased robustness and availability ofthe system. The system can be integrated with an existing system that investigatesdriver behavior, developed for fatigue. That system was developed in aproject named Drowsi, where among others Volvo Technology participated. / Ett filpositioneringssystem undersöks och förbättringstekniker för ökandet av robusthetoch tillgängligheten av ett sådant system genom att använda ytterligaresensorkällor som kartdata och GPS. Detta examensarbete presenterar beskrivningenav ett system, två modeller och två implementerade filter. Examensarbetetinnehåller också slutsatser och resultat av teoretiska och experimentella testersom plottar och grafer av ökad robusthet och tillgängligheten av systemet. Dettasystem kan bli integrerat med ett framtaget system som tittar på körrelaterat beteendevid trötthet. Systemet är utvecklat i ett projekt kallat Drowsi, där blandandra Volvo Technology deltog.

Approved for public release; distribution is unlimited / LosCon, the software program developed for the author's thesis and tested at sea, is designed to help the ASW commander regain tactical control in a loss of submarine contact situation. Persistent detection and cueing in the battlespace depend on utilizing contact reports from a network of combatant platform and offboard sensors. LosCon, an extended Kalman filter-based program modeled after MTST (Maneuvering Target Statistical Tracker), can integrate the sensor network very efficiently. Kalman filtering is a method of recursively updating the position of an evading target and accuracy of that position using imperfect measurements. Lines of bearing to the contact with associated standard deviation bearing errors and positions with their standard deviation range errors are the measurements LosCon uses to generate an ellipse of the submarine's likely position or AOU (Area Of Uncertainty). LosCon will also generate an expanded AOU for any future time, allowing commanders to correctly estimate the size of the search area. The effectiveness of the sea shield concept depends on the ability of organic forces to deny the enemy tactical control of the battlespace area. Incorporating the information generated by LosCon would assist ASW commanders in maintaining undersea superiority. / Ensign, United States Navy

[pt] Este trabalho aborda o problema da Localização e Mapeamento Simultâneos em ambientes estruturados, utilizando um robô móvel equipado com sonares, bússola eletrônica e encoders. Na modelagem sugerida há a construção do mapa do ambiente e a localização do robô de forma interativa. O método proposto, denominado de LMS-H (Localização e Mapeamento Simultâneos - Híbrido), faz uso de duas formas de representação do ambiente: Mapa de Ocupação em Grade e Representação Contínua. O Mapa de Ocupação em Grade divide o ambiente em pequenas partes iguais, classificando-as em ocupadas ou vazias. A Representação Contínua utiliza retas para representar os planos detectados no ambiente, formando um mapa em duas dimensões e cada reta do mapa é considerada um marco. Sempre que um plano é novamente detectado pelo robô a reta correspondente a ele é recalculada com os novos pontos obtidos e a posição do robô é atualizada via Filtro de Kalman Estendido. A eficácia do método foi comprovada através de seis estudos de caso: três em ambientes virtuais e três em ambientes reais. Os estudos de casos em ambientes reais foram realizados utilizando-se um protótipo feito sob a plataforma LEGO Mindstorms. Os resultados obtidos comprovaram a eficácia do método proposto. / [en] This work addresses the problem of Simultaneous Localization and Mapping in structured environments using a mobile robot equipped with sonar, electronic compass and encoders. In the proposed modeling there are the construction of the environment map and the robot localization interactively. The proposed method, called H-SLAM (Hybrid - Simultaneous Localization and Mapping), makes use kinds of environment representation: Occupancy Grid Map and Continuous Representation. The Occupancy Grid Map divides the environment into small equal parts, and classifies it as occupied or empty. The Continuous Representation uses lines to represent detected planes in the environment, forming a two-dimensional map. Each line of the map is considered a landmark. Every time a plan is redetected by the robot the corresponding line to it is rebuild with the new points obtained and the robot s position is updated through Extended Kalman Filter. The model effectiveness was proved with computer simulations in three virtual environments. Using a prototype developed with LEGO Mindstorms platform three other experiments were also performed in real environments. The results demonstrated the effectiveness of the proposed method.

Este trabalho apresenta através de simulação um estudo comparativo de estimadores de velocidade de motor de indução trifásico por meio de observadores de estado e da técnica do filtro de Kalman. É realizada uma análise comparativa de desempenho das estratégias de estimação determinísticas e estocásticas, com observadores adaptativos e estimadores baseados na teoria do filtro de Kalman estendido, respectivamente. A realização do trabalho visa a constatação dos procedimentos de elaboração, de operação e de aplicação destas técnicas de estimação usando um exemplo real com fins de ilustrar o ensino de controle e acionamento de máquinas elétricas. As simulações foram realizadas através do Matlab/Simulink com a utilização das ferramentas do Power System Blockset (PSB) e o algoritmo dos estimadores é escrito em programa Matlab e executado por uma função S-Function. Os resultados de simulação demonstram a eficiência de cada um dos estimadores propostos, no que se refere ao comportamento transitório, robustez a ruídos e variações nos parâmetros do motor. / This works presents through of the simulation a comparative study of the sensorless of speed estimation of induction three-phase motor using state observer and Kalman filter. A comparative analysis of the performance of the deterministic and stochastic estimation strategies using adaptive observers and estimators based on extended Kalman filter was realized. The work aims to verify the procedure of the elaboration, operation and application of such estimation techniques using a real example to illustrate the teaching of the control and driving of electric machines. The simulations where performed using Matlab/Simulink with Power System Blockset (PSB) toolboxes and the estimators are programmed as S-Function Matlab. The results indicate the effectiveness of the proposed estimators, according to the transient behavior, robustness to noise and ability to handle parametric variations.