Posicionamento em ambientes não estruturados e treinamento de redes neurais utilizando filtros de Kalman

• Ano de defesa: 2016
• Autor(a) principal: Lima, Denis Pereira de
• Orientador(a): Kato, Edilson Reis Rodrigues
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• Palavras-chave em Português: Filtros de Kalman adaptativos; Séries caóticas; Unscented Kalman Filter; Redes neurais; Filtro de Kalman estendido
• Palavras-chave em Inglês: Chaotic series; Unscented kalman filter; Neura networks; Extended kalman filter; State estimation; Unstructured environment
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/7874

Kalman filters are rooted in the technical literature, as a way of predicting new states in nonlinear systems providing a recursive solution to the problem of linear optimal filtering. Therefore, 56 years after its discovery, many modifications have been proposed in order to obtain better accuracy and speed. Some of these changes are used in this work; these being the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Kalman Filter Cubature (CKF). This work , divided into three distinct parts: Implementation / Comparative analysis of prediction of Kalman filters in complex systems (Series), qualitative analysis of the possible uses of the Kalman filter variants for neural network training and position and velocity determination a displaced object on a simulated plane with some trajectories Having these analyzes key role in fostering the studies cited in the scientific literature , proving the possibility of such algorithms and methods are used for positioning in unstructured environments