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State estimation for discrete-time markov jump linear systems based on orthogonal projective theorem
In this paper, state estimation problem for discrete-time Markov jump linear systems is considered. Based on orthogonal projective theorem, a novel suboptimal algorithm for state estimate of discrete-time Markov jump linear systems in the sense of minimum mean square error estimate is proposed. The...
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Published in: | International journal of control, automation, and systems 2012, Automation, and Systems, 10(5), , pp.1049-1054 |
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container_title | International journal of control, automation, and systems |
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creator | Liu, Wei Zhang, Huaguang Wang, Zhanshan Sun, Qiuye |
description | In this paper, state estimation problem for discrete-time Markov jump linear systems is considered. Based on orthogonal projective theorem, a novel suboptimal algorithm for state estimate of discrete-time Markov jump linear systems in the sense of minimum mean square error estimate is proposed. The proposed suboptimal algorithm is recursive and finite-dimensionally computable. Computer simulations are carried out to evaluate the performance of the proposed suboptimal algorithm. |
doi_str_mv | 10.1007/s12555-012-0523-1 |
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Based on orthogonal projective theorem, a novel suboptimal algorithm for state estimate of discrete-time Markov jump linear systems in the sense of minimum mean square error estimate is proposed. The proposed suboptimal algorithm is recursive and finite-dimensionally computable. 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subjects | Algorithms Analysis Automation Control Control systems Control theory Engineering Estimates Hypotheses Information science Kalman filters Linear systems Load Markov analysis Markov processes Mean square errors Mechatronics Noise Performance evaluation Recursion theory Recursive Robotics State estimation Studies Theorems 제어계측공학 |
title | State estimation for discrete-time markov jump linear systems based on orthogonal projective theorem |
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