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State estimation for asynchronous multirate multisensor nonlinear dynamic systems with missing measurements
Author(s) -
Yan Liping,
Xiao Bo,
Xia Yuanqing,
Fu Mengyin
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.2266
Subject(s) - kalman filter , control theory (sociology) , bernoulli's principle , nonlinear system , asynchronous communication , sampling (signal processing) , computer science , bernoulli distribution , extended kalman filter , state (computer science) , filter (signal processing) , point (geometry) , sigma , algorithm , mathematics , engineering , artificial intelligence , statistics , random variable , telecommunications , physics , geometry , control (management) , quantum mechanics , computer vision , aerospace engineering
SUMMARY This paper is concerned with the state estimation for a kind of nonlinear multirate multisensor asynchronous sampling dynamic system. There are N sensors observing a single target independently at multiple sampling rates, and the dynamic system is formulated at the highest sampling rate. Observations are obtained asynchronously, and each sensor may lose data randomly at a certain probability. The fused state estimate is generated using multiscale system theory and the modified sigma point Kalman filter. It is shown that our main results improve and extend the existing sigma point Kalman filter for which the samples are obtained multirate nonuniformly. Measurements randomly missing with Bernoulli distribution could also be allowed in this paper. Finally, the feasibility and efficiency of the presented algorithm is illustrated by a numerical simulation example.Copyright © 2012 John Wiley & Sons, Ltd.