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