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

<!DOCTYPE html> <html lang="en" class="no-js"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes"> <title>Smoothed estimation of unknown inputs and states in dynamic systems with application to oceanic flow field reconstruction</title> <meta id="meta-title" property="citation_title" content="Smoothed estimation of unknown inputs and states in dynamic systems with application to oceanic flow field reconstruction"/> <meta id="og-title" property="og:title" content="Smoothed estimation of unknown inputs and states in dynamic systems with application to oceanic flow field reconstruction"/> <meta name="twitter:widgets:autoload" content="off"/> <meta name="twitter:dnt" content="on"/> <meta name="twitter:widgets:csp" content="on"/> <meta name="google-site-verification" content="lQbRRf0vgPqMbnbCsgELjAjIIyJjiIWo917M7hBshvI"/> <meta id="meta-abstract" name="citation_abstract" content="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 &amp; Sons, Ltd."/> <meta id="meta-description" name="description" content="Author(s): Fang, Huazhen; de Callafon, Raymond A; Franks, Peter JS | 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 © 2014 John Wiley &amp; Sons, Ltd."/> <meta id="og-description" name="og:description" content="Author(s): Fang, Huazhen; de Callafon, Raymond A; Franks, Peter JS | 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. 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</li><li><a href="/search/?q=author%3Ade%20Callafon%2C%20Raymond%20A">de Callafon, Raymond A</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AFranks%2C%20Peter%20JS">Franks, Peter JS</a> </li></ul></div><div class="c-pubinfo"><h2 class="c-pubinfo__location-heading">Published Web Location</h2><a href="https://doi.org/10.1002/acs.2529" class="c-pubinfo__link">https://doi.org/10.1002/acs.2529</a></div><div class="c-tabs"><div class="c-tabs__tabs"><button class="c-tabs__button-more" aria-label="Show all tabs">...</button><button class="c-tabs__button--active">Main Content</button><button class="c-tabs__button">Metrics</button><button class="c-tabs__button">Author & Article Info</button></div><div class="c-tabs__content"><div class="c-tabcontent"><a name="article_abstract"></a><details class="c-togglecontent" open=""><summary>Abstract</summary><div class="c-clientmarkup"><p>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.</p></div><p class="o-well-colored">Many UC-authored scholarly publications are freely available on this site because of the UC's <a href="https://osc.universityofcalifornia.edu/open-access-at-uc/open-access-policy/">open access policies</a>. <a href="https://help.escholarship.org/support/tickets/new">Let us know how this access is important for you.</a></p></details><details class="c-togglecontent" open=""><a name="article_main"></a><summary>Main Content</summary><div class="c-pdfview"><button class="c-pdfview__button-download">Download PDF to View</button><button class="c-pdfview__button-view">View Larger</button></div><div class="c-pdfview__accessibility">For improved accessibility of PDF content, <a href="/content/qt1xq35553/qt1xq35553.pdf?t=phruom">download the file</a> to your device.</div><div class="c-pdfview__viewer"><div id="pdfjs-cdl-wrapper"> <div id="pdfjs-viewer" className="jsonly"> <noscript> <embed 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<span data-l10n-id="print_progress_percent" data-l10n-args='{ "progress": 0 }' class="relative-progress">0%</span> </div> <div class="buttonRow"> <button id="printCancel" class="overlayButton"><span data-l10n-id="print_progress_close">Cancel</span></button> </div> </div> </div>     </div>  </div>  <div id="printContainer"></div> </div>  </div></div></details></div><p><br/></p></div></div></main><aside><section class="o-columnbox1"><header><h2>Jump To</h2></header><div class="c-jump"><a href="/uc/item/1xq35553">Article</a><ul class="c-jump__tree" aria-labelledby="c-jump__label"><li><a href="/uc/item/1xq35553">Smoothed estimation of unknown inputs and states in dynamic systems with application to oceanic flow field reconstruction</a></li><li><a href="/uc/item/1xq35553">SUMMARY</a></li><li><a href="/uc/item/1xq35553">Introduction</a></li><li><a href="/uc/item/1xq35553">Bayesian Input and State Smoothing</a></li><li><a href="/uc/item/1xq35553">Smoothing Algorithms</a></li><li><a href="/uc/item/1xq35553">Extension to Systems without Direct Feedthrough</a></li><li><a href="/uc/item/1xq35553">Application Example</a></li><li><a href="/uc/item/1xq35553">Conclusions</a></li><li><a href="/uc/item/1xq35553">REFERENCES</a></li></ul><ul class="c-jump__siblings"><li><a href="/uc/item/1xq35553">Metrics</a></li><li><a href="/uc/item/1xq35553">Author & Article Info</a></li></ul></div></section><div><section class="o-columnbox1"><header><h2>Related Items</h2></header><ul class="c-relateditems"><li><h3><a class="o-textlink__secondary" href="/uc/item/04d6g2sr"><div class="c-clientmarkup">The Somali Distress and Resilience Scale: Development of a novel measure for Somali adults.</div></a></h3><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3ATerrana%2C%20Alec">Terrana, Alec</a>; </li><li><a href="/search/?q=author%3ABruno%2C%20William">Bruno, William</a>; </li><li><a 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Smoothed estimation of unknown inputs and states in dynamic systems with application to oceanic flow field reconstruction