Computationally Efficient Kalman Filtering for a Class of Nonlinear Systems

Author

Charalampidis, Alexandros C. ; Papavassilopoulos, George P.

Author_Institution

Sch. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens, Athens, Greece

Volume

56

Issue

3

fYear

2011

fDate

3/1/2011 12:00:00 AM

Firstpage

483

Lastpage

491

Abstract

This paper deals with recursive state estimation for the class of discrete time nonlinear systems whose nonlinearity consists of one or more static nonlinear one-variable functions. This class contains several important subclasses. The special structure is exploited to permit accurate computations without an increase in computational cost. The proposed method is compared with standard Extended Kalman Filter, Unscented Kalman Filter and Gauss-Hermite Kalman Filter in three illustrative examples. The results show that it yields good results with small computational cost.

Keywords

Kalman filters; covariance matrices; discrete time systems; nonlinear control systems; nonlinear filters; state estimation; state-space methods; Gauss-Hermite Kalman filter; discrete time nonlinear system; extended Kalman filter; recursive state estimation; static nonlinear one variable function; unscented Kalman filter; Covariance matrices; nonlinear filters; numerical methods; recursive state estimation; state space methods; unscented Kalman filtering;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/TAC.2010.2078090

Filename

5582210