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

页码：1224 / 1242

页数：19

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