By Topic

State estimation in non-linear markov jump systems with uncertain switching probabilities

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $33
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
S. Zhao ; Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University,Wuxi 214122, People's Republic of China ; F. Liu

In this article, a study of state estimation for non-linear Markov jump systems (MJSs) with uncertain transition probabilities (TPs) is investigated. In the authors' method, the uncertainties of TPs are portrayed by intermediate stochastic variables depicted by truncated Gaussian probability density functions (TGPDFs). In order to incorporate the prior knowledge about uncertainties into the filtering process, a skew parameter is firstly inserted into TGPDF to yield skew truncated Gaussian probability density functions (STGPDFs) which contains the original one as a particular case. Then, the state estimation method is derived based on multiple model mechanism together with particle filter using confidence TPs that are obtained by normalising the expectations of STGPDFs. The proposed approach degenerates into the traditional interacting multiple model-particle filter (IMM-PF) when the standard deviations turn to zero. A meaningful example is presented to illustrate the effectiveness of the authors' method.

Published in:

IET Control Theory & Applications  (Volume:6 ,  Issue: 5 )