Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14382
Title: State smoothing in Markov jump systems with lagged mode observation
Authors: Liang, Y
Zhang, L 
Pan, Q
Chen, T
Keywords: adaptive estimation
asynchronous fusion
Markov jump systems
target tracking
Issue Date: 2010
Publisher: John Wiley & Sons Ltd
Source: International journal of adaptive control and signal processing, 2010, v. 24, no. 11, p. 1005-1020 How to cite?
Journal: International Journal of Adaptive Control and Signal Processing 
Abstract: 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.
URI: http://hdl.handle.net/10397/14382
DOI: 10.1002/acs.1168
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