Smoothed estimation of unknown inputs and states in dynamic systems with application to oceanic flow field reconstruction | Zendy

Fang Huazhen | Zendy; Callafon Raymond A. | Zendy; Franks Peter J. S. | Zendy

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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.

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