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A new comparison of Kalman filtering methods for chaotic series

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, 54 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, intense research demonstrate the performance of each of their possible modifications and improvements, such as the use of Neural Networks in order to obtain an approximate model of the real system. The objective of this work is to use a known model, and testing eight possible modifications of these algorithms, thus obtaining better algorithm for future implementation with Neural Networks (NN), this being used for servo positioning in unstructured environments.