Nonlinear Bayesian estimation: From Kalman filtering to a broader horizon

Document Type

Article

Publication Date

3-1-2018

Abstract

This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date, one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective, which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics U+0028 e.g., mean and covariance U+0029 conditioned on a system U+02BC s measurement data. This article offers a systematic introduction to the Bayesian state estimation framework and reviews various Kalman filtering U+0028 KF U+0029 techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation to more complicated problems such as simultaneous state and parameter U+002F input estimation.

Identifier

85041915565 (Scopus)

Publication Title

IEEE Caa Journal of Automatica Sinica

External Full Text Location

https://doi.org/10.1109/JAS.2017.7510808

e-ISSN

23299274

ISSN

23299266

First Page

401

Last Page

417

Issue

2

Volume

5

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