Precomputed-gain nonlinear filters for nonlinear systems with state-dependent noise

Research output: Contribution to conferencePaperpeer-review

Abstract

Two precomputed-gain nonlinear filters are proposed for estimating the states of nonlinear systems corrupted by both external and parametric noises and subjected to linear noisy measurement systems. The exact nonlinear filters are first formulated using the Kolmogorov and Kushner equations. The concept of equivalent external excitation combined with statistical linearization or local linearization is then employed to derive two precomputed-gain nonlinear filters. The resulting filters are shown to have the same structure as the extended Kalman filter but filter-gain histories can be precomputed. Monte-Carlo simulation results for the proposed nonlinear filters and the corresponding linear filters are compared for Duffing-type stochastic systems.

Original languageEnglish
Pages2639-2645
Number of pages7
DOIs
Publication statusPublished - 1989
EventProceedings of the 1989 American Control Conference - Pittsburgh, PA, USA
Duration: 1989 Jun 211989 Jun 23

Other

OtherProceedings of the 1989 American Control Conference
CityPittsburgh, PA, USA
Period89-06-2189-06-23

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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