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Risk‐sensitive filtering for nonlinear Markov jump systems on the basis of particle approximation
Author(s) -
Zhao Shunyi,
Liu Fei,
Luan Xiaoli
Publication year - 2012
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.1284
Subject(s) - particle filter , nonlinear system , resampling , robustness (evolution) , hidden markov model , computer science , filtering problem , jump , filter (signal processing) , gaussian , basis (linear algebra) , markov process , auxiliary particle filter , markov chain , control theory (sociology) , mathematical optimization , algorithm , mathematics , artificial intelligence , filter design , statistics , kalman filter , machine learning , ensemble kalman filter , physics , extended kalman filter , chemistry , biochemistry , control (management) , quantum mechanics , computer vision , gene , geometry
SUMMARY In this paper, the risk‐sensitive filtering method that relaxes the dependence on model accuracy is extended to nonlinear Markov jump systems (MJSs). In the method, the so‐called reference probability technique together with particle approximation is utilized to derive the risk‐sensitive filter in nonlinear non‐Gaussian framework. The novelty of the proposed approach is that a ‘risky’ interacting resampling step is performed to both moderate the modeling uncertainties and to solve the problem of particle explosion. A designer‐chosen parameter named risk‐sensitive parameter allows us to make a trade‐off between the filtering accuracy for the nominal model and the robustness to uncertainties. With a meaningful example, it shows that the developed method can outperform the widely used method‐particle filter and interacting multiple model‐particle filter in nonlinear MJSs with uncertainties. Copyright © 2011 John Wiley & Sons, Ltd.