Nonlinear state estimation, indistinguishable states, and the extended Kalman filter
type: Item
instance of: scholarly article
title: Nonlinear state estimation, indistinguishable states, and the extended Kalman filter
zbMATH Open document ID: 1036.37034
DOI: 10.1016/S0167-2789(03)00180-5
author: Kevin Judd
published in: Physica D
publication date: 2003-08-24T00:00:00Z
review text: For estimating on states of nonlinear systems, the gradient descent filter (GDF) is introduced and compared with the widely used extended Kalman filter (EKF). It is found, that GDF performs better when observational noise is large. However, EKF performs better when there is a low level of the observational noise and a high level of dynamic noise.
Mathematics Subject Classification ID: 37N35
Mathematics Subject Classification ID: 37D45
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 93E10
zbMATH DE Number: 1969777
zbMATH Keywords: nonlinear dynamical systems
zbMATH Keywords: state estimation
zbMATH Keywords: forecasting
zbMATH Keywords: extended Kalman filter
zbMATH Keywords: gradient descent filter
zbMATH Keywords: nonlinear noise reduction
reviewed by: Grigori N. Milstein
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 1969777