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

TitleSmoothed estimation of unknown inputs and states in dynamic systems with application to oceanic flow field reconstruction
Publication TypeJournal Article
Year of Publication2015
AuthorsFang H.Z, de Callafon R.A, Franks PJS
JournalInternational Journal of Adaptive Control and Signal Processing
Volume29
Pagination1224-1242
Date Published2015/10
Type of ArticleArticle
ISBN Number0890-6327
Accession NumberWOS:000362458100002
Keywordsdiscrete-time-systems; filters; forward-backward smoothing; input estimation; minimum-variance estimation; nonlinear systems; nonlinear-systems; ocean observing; state estimation
Abstract

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 (c) 2014 John Wiley & Sons, Ltd.

DOI10.1002/acs.2529
Student Publication: 
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