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CLC number: TP13
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Received: 2005-12-20
Revision Accepted: 2006-04-10
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A filter algorithm for multi-measurement nonlinear system with parameter perturbation
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GUO Yun-fei, WEI Wei, XUE An-ke, MAO Dong-cai. A filter algorithm for multi-measurement nonlinear system with parameter perturbation[J]. Journal of Zhejiang University Science A, 2006, 7(10): 1717-1722.
@article{title="A filter algorithm for multi-measurement nonlinear system with parameter perturbation",
author="GUO Yun-fei, WEI Wei, XUE An-ke, MAO Dong-cai",
journal="Journal of Zhejiang University Science A",
volume="7",
number="10",
pages="1717-1722",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A1717"
}
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%T A filter algorithm for multi-measurement nonlinear system with parameter perturbation
%A GUO Yun-fei
%A WEI Wei
%A XUE An-ke
%A MAO Dong-cai
%J Journal of Zhejiang University SCIENCE A
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%N 10
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%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A1717
TY - JOUR
T1 - A filter algorithm for multi-measurement nonlinear system with parameter perturbation
A1 - GUO Yun-fei
A1 - WEI Wei
A1 - XUE An-ke
A1 - MAO Dong-cai
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 10
SP - 1717
EP - 1722
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.A1717
Abstract: An improved interacting multiple models particle filter (IMM-PF) algorithm is proposed for multi-measurement nonlinear system with parameter perturbation. It divides the perturbation region into sub-regions and assigns each of them a particle filter. Hence the perturbation problem is converted into a multi-model filters problem. It combines the multiple measurements into a fusion value according to their likelihood function. In the simulation study, we compared it with the IMM-KF and the H-infinite filter; the results testify to its advantage over the other two methods.
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