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A nonlinear filter is presented by using a formal linearization method and the Extended Kalman Filter (EKF) approach in this paper. Defining a linearization function that consists of polynomials, a given nonlinear dynamic system is transformed into an augmented linear one with respect to this linearization function. Introducing a new augmented measurement vector that consists of polynomials of measurement data for a given measurement equation, this equation is also transformed into an augmented linear one with respect to the linearization function in the same way. As a result, the EKF theory can be applied to these augmented linearized systems and a nonlinear filter is synthesized. In order to show the performance of the method, numerical experiments are carried out by comparing with the EKF as a conventional method.
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Komatsu et al. (Thu,) studied this question.
www.synapsesocial.com/papers/68e76e6eb6db6435876e4683 — DOI: https://doi.org/10.2299/jsp.28.37
Kazuo Komatsu
Hitoshi Takata
Journal of Signal Processing
Kagoshima University
National Institute of Technology, Kumamoto College
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