Smoothed estimation of unknown inputs and states in dynamic systems with application to oceanic flow field reconstruction
作者： Huazhen Fang , Raymond A. de Callafon , Peter J. S. Franks
DOI: 10.1002/ACS.2529
关键词:
摘要: Author(s): Fang, H; de Callafon, RA; Franks, PJS | Abstract: Copyright © 2014 John Wiley a Sons, Ltd. 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 swarm buoyancy-controlled drifters. The development conducted Bayesian framework. A paradigm constructed first to offer probabilistic view the quantities given measurements. Then maximum posteriori established build means simultaneous smoothing, which can be solved classical Gauss–Newton method nonlinear case. Application complex three-dimensional presented investigated via simulation studies.
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