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Received:October 19, 2009Revised:October 09, 2010 |
基金项目:This work was supported by the National Natural Science Foundation of China (No. 60874044), and the Doctoral Fundation of Ministry of Education (No. 20111102110006). |
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Quasi-equality constrained risk-sensitive filtering for nonlinear discrete-time systems |
Linfeng SHEN,Yan LIN |
(School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics) |
Abstract: |
More and more data fusion models contain state constraints with valuable information in the filtering process. In this study, an optimal filter of risk-sensitive with quasi-equality constraints is formulated using the reference probability method. Through recursion processes of probability density acquired from the probability measure change, the derived algorithm is optimal in the sense of the risk-sensitive parameter. The system and constraint models are consistent in statistics. Simulation results show that it is more robust and efficient than projection filters for the worst-case of noises and model uncertainty. |
Key words: Optimal estimation Risk-sensitive filtering Constraints |