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State smoothing in Markov jump systems with lagged mode observation
Author(s) -
Liang Yan,
Zhang Lei,
Pan Quan,
Chen Tongwen
Publication year - 2010
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.1168
Subject(s) - mode (computer interface) , smoothing , estimator , tracking (education) , hidden markov model , state (computer science) , computer science , jump , computation , markov chain , control theory (sociology) , algorithm , artificial intelligence , mathematics , statistics , computer vision , machine learning , psychology , pedagogy , physics , control (management) , quantum mechanics , operating system
Estimation involving Markov jump systems (MJSs) is widely used in target tracking, speech recognition and communication. It is assumed in MJSs that state measurement and mode observation are synchronous. In applications such as image‐based target tracking, the target orientation, as one of the mode observations, needs additional computation time for pattern recognition and thus can be delayed. This motivates us to explore the smoothing problem of MJSs with mode observation lagged to state measurement. This brief paper presents a recursive estimator by deriving the conditional state mean and the conditional model probability from both delayed mode observation and state measurement. Simulations on maneuvering target tracking are carried out to validate the performance of the proposed smoother in comparison with existing methods. Copyright © 2010 John Wiley & Sons, Ltd.