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Smoothed estimation of unknown inputs and states in dynamic systems with application to oceanic flow field reconstruction
Author(s) -
Fang Huazhen,
Callafon Raymond A.,
Franks Peter J. S.
Publication year - 2015
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.2529
Subject(s) - smoothing , maximum a posteriori estimation , nonlinear system , field (mathematics) , computer science , bayesian probability , flow (mathematics) , algorithm , probabilistic logic , state (computer science) , mathematical optimization , dynamic bayesian network , a priori and a posteriori , control theory (sociology) , mathematics , artificial intelligence , computer vision , maximum likelihood , statistics , philosophy , control (management) , epistemology , physics , geometry , quantum mechanics , pure mathematics
Summary Forward‐backward smoothing based unknown input and state estimation for dynamic systems is studied in this paper, motivated by reconstruction of an oceanographic flow field using a swarm of buoyancy‐controlled drifters. The development is conducted in a Bayesian framework. A Bayesian paradigm is constructed first to offer a probabilistic view of the unknown quantities given the measurements. Then a maximum a posteriori is established to build a means for simultaneous input and state smoothing, which can be solved by the classical Gauss–Newton method in the nonlinear case. Application to reconstruction of a complex three‐dimensional flow field is presented and investigated via simulation studies. Copyright © 2014 John Wiley & Sons, Ltd.