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

• Autor(a) principal: Lima, Denis Pereira de
• Data de Publicação: 2016
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/7874
• topic: Filtros de Kalman adaptativos; Séries caóticas; Unscented Kalman Filter; Redes neurais; Filtro de Kalman estendido; Chaotic series; Unscented kalman filter; Neura networks; Extended kalman filter; State estimation; Unstructured environment; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

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