Please use this identifier to cite or link to this item:
Title: Convergence analysis of nonlinear Kalman filters with novel innovation-based method
Authors: Wang, S
Wang, W
Chen, B
Tse, CK 
Keywords: Convergence analysis
Estimation error
Linear measurements
Nonlinear Kalman filters
Issue Date: 2018
Publisher: Elsevier
Source: Neurocomputing, 2018, v. 289, p. 188-194 How to cite?
Journal: Neurocomputing 
Abstract: The convergence of nonlinear Kalman filters has conventionally been analyzed in terms of the estimation error. In this paper, we present a new method for investigating the convergence performance of a class of nonlinear Kalman filters based on deterministic sampling. The systems considered here are described by nonlinear state equations with linear measurements. For this type of systems, our proposed convergence analysis is performed using “innovation” which is defined as the error between the measurement and its prediction. Specifically, we obtain a linear relationship between the innovation and the estimation error, and derive a set of sufficient conditions that ensures the convergence of nonlinear Kalman filters. Compared with the conventional convergence analysis method based on the estimation error, the proposed innovation-based method can obtain sufficient conditions for convergence more directly and readily. Simulation results show that the convergence of innovation generates the convergence of nonlinear Kalman filters.
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2018.02.001
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Citations as of Sep 18, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.