Thesis (M.S.)--California Polytechnic State University, 2009. / Mode of access: Internet. Title from PDF title page; viewed on Jan. 19, 2010. Major professor: John Saghri, Associate Professor. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Electrical Engineering." "December 2009." Includes bibliographical references (p. 67-68).

Tsunami concerns have increased in the world after the 2004 Indian Ocean tsunami and the 2011 Tohoku tsunami. Consequently, tsunami models have been developed rapidly in the last few years. One of the advanced tsunami models is the GeoClaw tsunami model introduced by LeVeque (2011). This model is adaptive and consistent. Because of different sources of uncertainties in the model, observations are needed to improve model prediction through a data assimilation framework. Model inputs are earthquake parameters and topography. This thesis introduces a real-time tsunami forecasting method that combines tsunami model with observations using a hybrid ensemble Kalman filter and ensemble Kalman smoother. The filter is used for state prediction while the smoother operates smoothing to estimate the earthquake parameters. This method reduces the error produced by uncertain inputs. In addition, state-parameter EnKF is implemented to estimate earthquake parameters. Although number of observations is small, estimated parameters generates a better tsunami prediction than the model. Methods and results of prediction experiments in the Red Sea are presented and the prospect of developing an operational tsunami prediction system in the Red Sea is discussed.

A white Gaussian noise measurement model is widely used in target tracking problem formulation. In practice, the measurement noise may not be white. This phenomenon is due to the scintillation of the target. In many radar systems, the measurement frequency is high enough so that the correlation cannot be ignored without degrading tracking performance. In this thesis, target tracking problem with correlated measurement noise is considered. The correlated measurement noise is modeled by a first-order Markov model. The effect of correlation is thought as interference, and Optimum Decoding Based Smoothing Algorithm is applied. For linear models, the estimation performances of Optimum Decoding Based Smoothing Algorithm are compared with the performances of Alpha-Beta Filter Algorithm. For nonlinear models, the estimation performances of Optimum Decoding Based Smoothing Algorithm are compared with the performances of Extended Kalman Filter by performing various simulations.

This thesis presents the design of Kalman filter based attitude determination algorithms for a hypothetical LEO satellite and for a satellite attitude control test setup. For the hypothetical LEO satellite, an Extended Kalman Filter based attitude determination algorithms are formed with a multi-mode structure that employs the different sensor combinations and as well as online switching between these combinations depending on the sensor availability. The performance of these different attitude determination modes are investigated through Monte Carlo simulations. New attitude determination algorithms are prepared for the satellite attitude control test setup by considering the constraints on the selection of the suitable sensors. Here, performances of the Extended Kalman Filter and Unscented Kalman Filter are investigated. It is shown that robust and sufficiently accurate attitude estimation for the test setup is achievable by using the Unscented Kalman Filter.

This thesis considers the filtering and prediction problems of nonlinear noisy econometric systems. As a filter/predictor, the standard tool Extended Kalman Filter and new approaches Discrete Quantization Filter and Sequential Importance Resampling Filter are used. The algorithms are compared by using Monte Carlo Simulation technique. The advantages of the new algorithms over Extended Kalman Filter are shown.

Made available in DSpace on 2016-08-17T14:52:45Z (GMT). No. of bitstreams: 1 Jose Alano Peres Abreu.pdf: 632239 bytes, checksum: 326cfda664cdb5244eb2f9f6331fb1fe (MD5) Previous issue date: 2002-12-13 / In this work, a new methodology of filtering data of paths of space vehicles is proposed H2 and H∞ saw state estimates and discreet. In that new methodology, it is obtained, initially, the solution of the problem of filtering of data of paths of space vehicles saw state estimate through the equations of the filter of Kalman for Predicted Estimators and Filtered Estimators. The problem is solved through the mathematical development of the equations of the filter of Kalman that has as main function, to find a state estimate that minimizes the least-squares error. The equations mathematics are used for the development of the algorithm of the filter of Kalman. The algorithm of filtering of Kalman has two basic functions: prediction and correction. In the prediction phase the initial estimates and updating of the time of sampling are given, while, in the correction phase they are updated the measures. It is applied, also, the new methodology proposed in the project of filtering of data of path of space vehicles H∞ saw state estimate through equations of robust filter. The robust filtering has as function to esteem a linear combination that minimizes the norm, that has the interpretation of the existence of earnings of maximum energy of the entrance for the exit. In addition, it is obtained a new algorithm for filtering of data of paths of space vehicles, now through state estimate. All the project procedures are cultured through some applied examples to systems of tracking of space vehicles. The results are compared and discussed. / Neste trabalho, é proposta uma metodologia de filtragem de dados de trajetórias de veículos espaciais via estimações de estado H2 e H∞ , discretos. Nessa metodologia, obtém-se, inicialmente, a solução do problema de filtragem de dados de trajetórias de veículos espaciais via estimação de estado H2 através das equações do filtro de Kalman para Estimadores Filtrados. O problema é resolvido através do desenvolvimento matemático das equações do filtro de Kalman que tem como objetivo principal encontrar uma estimação de estado que minimize o erro quadrático médio. As equações matemáticas são utilizadas para o desenvolvimento do algoritmo computacional do filtro de Kalman. O algoritmo de filtragem de Kalman tem duas funções básicas: predição e correção. Na fase de predição são dadas as estimativas iniciais e atualização do tempo de amostragem, enquanto que, na fase de correção são atualizadas as medidas. Aplica-se, também, a nova metodologia proposta no projeto de filtragem de dados de trajetória de veículos espaciais via estimação de estado H∞ através de equações do filtro de Kalman robusto. A filtragem robusta tem como objetivo principal estimar uma combinação linear que minimize a norma H∞ , que tem a interpretação da existência de ganho de energia máxima da entrada para a saída. Como contribuição, obtém-se um novo algoritmo computacional para filtragem de dados de trajetórias de veículos espaciais, agora através de estimação de estado H∞ . Todos os procedimentos de projeto são ilustrados através de alguns exemplos aplicados a sistemas de rastreamento de veículos espaciais. Os resultados são comparados e discutidos.

Poškození zraku je jedním z nejčastějších tělesných postižení -- udává se, že až 3 % populace trpí vážným poškozením nebo ztrátou zraku. Oslepnutí výrazně zhoršuje schopnost orientace a pohybu v okolním prostředí -- bez znalosti uspořádání prostoru, jinak získané převážně pomocí zraku, postižený zkrátka neví, kudy se pohybovat ke svému cíli. Obvyklým řešením problému orientace v neznámých prostředích je doprovod nevidomého osobou se zdravým zrakem; tato služba je však velmi náročná a nevidomý se musí plně spolehnout na doprovod. Tato práce zkoumá možnosti, kterými by bylo možné postiženým ulehčit orientaci v prostoru, a to využitím existujících senzorických prostředků a vhodného zpracování jejich dat. Téma je zpracováno skrze analogii s mobilní robotikou, v jejímž duchu je rozděleno na část lokalizace a plánování cesty. Zatímco metody plánování cesty jsou vesměs k dispozici, lokalizace chodce často trpí značnými nepřesnostmi určení polohy a komplikuje tak využití standardních navigačních přístrojů nevidomými uživateli. Zlepšení odhadu polohy může být dosaženo vícero cestami, zkoumanými analytickou kapitolou. Předložená práce prvně navrhuje fúzi obvyklého přijímače systému GPS s chodeckou odometrickou jednotkou, což vede k zachování věrného tvaru trajektorie na lokální úrovni. Pro zmírnění zbývající chyby posunu odhadu je proveden návrh využití přirozených význačných bodů prostředí, které jsou vztaženy ke globální referenci polohy. Na základě existujících formalismů vyhledávání v grafu jsou zkoumána kritéria optimality vhodná pro volbu cesty nevidomého skrz městské prostředí. Generátor vysokoúrovňových instrukcí založený na fuzzy logice je potom budován s motivací uživatelského rozhraní působícího lidsky; doplňkem je okamžitý haptický výstup korigující odchylku směru. Chování navržených principů bylo vyhodnoceno na základě realistických experimentů zachycujících specifika cílového městského prostředí. Výsledky vykazují značná zlepšení jak maximálních, tak středních ukazatelů chyby určení polohy.

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.