Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22993
Title: Novel cubature Kalman filtering for systems involving nonlinear states and linear measurements
Authors: Wang, S
Feng, J
Tse, CK 
Keywords: Computational complexity
Convergence analysis
Cubature rules
Kalman filter
Matrix decompositions
Issue Date: 2014
Publisher: Urban und Fischer Verlag Jena
Source: AEU - International journal of electronics and communications, 2014, v. 69, no. 1, p. 314-320 How to cite?
Journal: AEU - International Journal of Electronics and Communications 
Abstract: This paper extends the cubature Kalman filter (CKF) to deal with systems involving nonlinear states and linear measurements (herein called the nonlinear-linear combined systems) with additive noise. The method is referred to as the nonlinear-linear square-root cubature Kalman filtering (NL-SCKF). In NL-SCKF, the cubature rule, combined with a QR decomposition, singular value decomposition and a linear update without requirement of cubature points, is designed to update nonlinear states and linear measurements. In addition, the convergence analysis of NL-SCKF is performed. Simulation results in two selected problems, namely filtering chaotic signals and chaos-based communications, indicate that the proposed NL-SCKF with lower computation complexity achieves the same accuracy as the standard SCKF, and outperforms CKF significantly.
URI: http://hdl.handle.net/10397/22993
ISSN: 1434-8411
DOI: 10.1016/j.aeue.2014.09.017
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

7
Last Week
0
Last month
0
Citations as of Dec 15, 2017

WEB OF SCIENCETM
Citations

5
Last Week
0
Last month
0
Citations as of Dec 12, 2017

Page view(s)

43
Last Week
1
Last month
Checked on Dec 11, 2017

Google ScholarTM

Check

Altmetric



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