The simulation and testing of several state tracking techniques over a range of process and measurement noise environments is considered. The problem is placed in the context of tracking a maneuvering vehicle from noisy position data with the vehicle accelerations considered as a random process about which the first and second order statistics are known. The tracking filters under test are the fixed α-β filter, the double α-β filter, the second order Kalman filter, the augmented Kalman filter, and the double Kalman filter. All filters show improved performance as the measurement noise increases and the process noise decreases. The superiority of the Kalman filter over the simpler deterministic digital trackers decreases as the measurement noise increases and the process noise decreases. The double Kalman filter, with the capability of adaptive adjustments of threshold values, indicates the best overall tracking for combined maneuver and non-maneuver tracking. / Master of Science

No description available.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2005. / Visually estimating three-dimensional position, orientation and motion, between an observer and a target, is an important problem in computer vision. Solutions which compute threedimensional movement from two-dimensional intensity images, usually rely on stereoscopic vision. Some research has also been done in systems utilising a single (monocular) camera. This thesis investigates methods for estimating position and pose from monocular image sequences. The intended future application is of visual tracking between satellites flying in close formation. The ideas explored in this thesis build on methods developed for use in camera calibration, and structure from motion (SfM). All these methods rely heavily on the use of different variations of the Kalman Filter. After describing the problem from a mathematical perspective we develop different approaches to solving the estimation problem. The different approaches are successfully tested on simulated as well as real-world image sequences, and their performance analysed.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1975 / Vita. / Includes bibliographical references. / Ph. D. / Ph. D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

In this thesis, a multiple hypotheses tracking (MHT) algorithm is developed to successfully track multiple ballistic missiles within the boost phase. The success of previous work on the MHT algorithm and its application in other scientific fields enables this study to realize an efficient form of the algorithm and examine its feasibility in tracking multiple crossing ballistic missiles even though various accelerations due to staging are present. A framework is developed for the MHT, which includes a linear assignment problem approach used to search the measurement-to-contact association matrix for the set of exact N-best feasible hypotheses. To test the new MHT, an event in which multiple ballistic missiles have been launched and threaten the North American continent is considered. To aid in the interception and destruction of the threat far from their intended targets, the research focuses on the boost-phase portion of the missile flight. The near-simultaneous attacks are detected by a network of radar sensors positioned near the missile launch sites. Each sensor provides position reports or track files for the MHT routine to process. To quantify the performance of the algorithm, data from the National Air and Space Intelligence Center's IMPULSE ICBM model is used and demonstrates the feasibility of this approach. This is especially significant to the U.S. Missile Defense Agency since the IMPULSE model represents the cognizant analyst's accurate representation of the ballistic threats in a realistic environment. The results show that this new algorithm works exceptionally well in a realistic environment where complex interactions of missile staging, non-linear thrust profiles and sensor noise can significantly degrade the track algorithm performance especially in multiple target scenarios.

Esta dissertação apresenta novos resultados para a solução de problemas de implementação computacional na estimativa de sistemas singulares e sistemas Markovianos. São apresentados algoritmos alternativos para problemas de filtragem de maneira a minimizar problemas causados principalmente por erros de arredondamento e mal condicionamento de matrizes. O trabalho envolve basicamente algoritmos array e filtragem de informação para a estimativa de sistemas singulares nominais e robustos. Também é deduzido um algoritmo array para a filtragem de sistemas lineares sujeitos a saltos Markovianos. / This dissertation presents new results to solve computational implementation problems to estimate singular and Markovian systems. Alternative algorithms to handle computational filtering errors due rounding errors and ill-conditioned matrices are developed. This dissertation comprehends basically array algorithms and information filters for the estimate of nominal and robust singular systems. Also, it is developed an array algorithm for Markovian jump linear systems filtering.

Made available in DSpace on 2015-03-04T18:57:45Z (GMT). No. of bitstreams: 1 Texto_completo_PArt1.pdf: 9176320 bytes, checksum: ad1d8c914089946e709acb58d1bae9d9 (MD5) Previous issue date: 2013-03-14 / Conselho Nacional de Desenvolvimento Cientifico e Tecnologico / This dissertation did a study on the impact of soil moisture data assimilation on the hydroclimatological variables for the Brazilian semiarid and the La Plata River Basin. The choice of this theme was a result of the understanding that the accuracy of estimating soil moisture is directly associated with the accuracy of the weather forecast and climate simulations. The regions, in turn, were chosen because, despite presenting extremely different geographic and socio-economic features with episodes of lack and/or excess water. In this context, it is understood that accurate, representative forecasts are of great importance in developing strategies for managing water use, so relevant to these areas. The study was conducted using NCEP's NOAH land surface model, responsible for describing the dynamics of soil water and generating the estimate of the variable studied as necessary for the assimilation scheme. In addition, we used, as observations for the amount of water in the soil, soil moisture records from the AMSR-E sensor, coupled to the AQUA satellite, and also from ground-based, automatic agrometeorological stations maintained by INPE (Brazilian National Institute for Space Research). These observations were used directly into the assimilation scheme. The Ensemble Kalman Filter was the method used to perform the data assimilation experiments. The last step was to investigate the temporal evolution of the terms, in the assimilation scheme, containing estimates of model and analysis errors. These terms are fundamental parts of the Kalman Filter formulation and have a great in uence in the calculation of the result and its quality. / Este trabalho fez um estudo sobre o impacto da assimilação de dados de umidade do solo nas variáveis hidroclimatológicas sobre o Semiárido brasileiro e a Bacia do Prata. A escolha deste tema foi consequência do entendimento de que a precisão da estimativa da umidade do solo está diretamente associada á precisão da previsão meteorológica nas escalas de tempo e clima. As regiões, por sua vez, foram escolhidas porque, apesar de apresentarem características geográficas e socio-economicas extremamente diferentes, apresentando episódios de falta e/ou excesso de água. Neste contexto, entende-se que previsões representativas auxiliam no desenvolvimento de estratégias de gerenciamento do uso da água, tão relevantes para estas áreas. O trabalho foi desenvolvido utilizando o modelo de superfície NOAH, responsável por descrever a dinâmica da água no solo e gerar a estimativa da variável estudada, necessária no esquema de assimilação. Além disso, foram usados os registros de umidade do solo, oriundos do sensor AMSR-E, acoplado ao satélite AQUA; e também das estações agrometeorológicas, mantidos pelo INPE, como observações para a quantidade de água no solo. Estas observações foram utilizadas, diretamente, no esquema de assimilação. O Filtro de Kalman por Conjuntos foi o método utilizado para implementar a assimilação de dados. O último passo consistiu em investigar a evolução temporal dos termos que, no esquema de assimilação, contém as estimativas dos erros do modelo e da análise. Estes termos são parte fundamental na formulação do filtro de Kalman e tem grande influência no cálculo do resultado e sua qualidade.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by James Brian Lewis. / M.S.