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Erschienen in:
1998 | ReviewPaper | Buchkapitel
State estimation for nonlinear systems using restricted genetic optimization
verfasst von : Santiago Garrido, Luis Moreno, Carlos Balaguer
Erschienen in: Methodology and Tools in Knowledge-Based Systems
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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In this paper we describe a new nonlinear estimator for filtering systems with nonlinear process and observation models, based on the optimization with RGO (Restricted Genetic Optimization). Simulation results are used to compare the performance of this method with EKF (Extended Kalman Filter), IEKF (Iterated Extended Kalman Filter), SNF (Second-order Nonlinear Filter), SIF (Single-stage Iterated Filter) and MSF (Monte-Carlo Simulation Filter) in the presence of diferents levels of noise.