Abstract
This paper proposes a one-step unscented particle filter for accurate nonlinear estimation. Its design involves the elaboration of a reliable one-step unscented filter that draws state samples deterministically for doing both the time and measurement updates, without linearization of the observation model. Empirical investigations show that the one-step unscented particle filter compares favourably to relevant filters on nonlinear dynamic systems modelling.
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Nikolaev, N.Y., Smirnov, E. (2007). A One-Step Unscented Particle Filter for Nonlinear Dynamical Systems. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4_76
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DOI: https://doi.org/10.1007/978-3-540-74690-4_76
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