A one-step unscented particle filter for nonlinear dynamical systems

Authors:
Nikolay Y. Nikolaev;Evgueni Smirnov
Affiliations:
Goldsmiths College, University of London, London, United Kingdom;MICC-IKAT, Maastricht University, Maastricht, The Netherlands
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 2
Cited 0

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Neural Computation
New developments in state estimation for nonlinear systems

Automatica (Journal of IFAC)

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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 onestep unscented particle filter compares favourably to relevant filters on nonlinear dynamic systems modelling.