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

被引：8
作者：
Fang, Huazhen [1 ]
de Callafon, Raymond A. [1 ]
Franks, Peter J. S. [2 ]
机构：
[1] Univ Calif San Diego, Dept Mech & Aerosp Engn, La Jolla, CA 92093 USA
[2] Univ Calif San Diego, Scripps Inst Oceanog, La Jolla, CA 92093 USA
来源：
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING | 2015年 / 29卷 / 10期
关键词：
input estimation; state estimation; forward-backward smoothing; nonlinear systems; ocean observing; MINIMUM-VARIANCE ESTIMATION; FILTERS;
D O I：
10.1002/acs.2529
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：1224 / 1242
页数：19
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