Robust Kalman filtering for nonlinear systems with parameter uncertainties

Author

Ishihara, Sayaka

Author_Institution

Hitachi Res. Lab., Ltd., Hitachi, Japan

fYear

2014

fDate

9-12 Sept. 2014

Firstpage

1986

Lastpage

1991

Abstract

This paper addresses state estimation problems for nonlinear systems with parameter uncertainties. A new robust unscented Kalman filter is devised by analyzing the influence which parameter uncertainties give to covariance matrix. Proposed method is one form of the DKF, but proposed method have a merit that designing weight matrix is easier than DKF in a certain situation. The validity of the proposed method is illustrated in Monte Carlo simulation.

Keywords

Kalman filters; Monte Carlo methods; covariance matrices; nonlinear filters; nonlinear systems; state estimation; DKF; Monte Carlo simulation; covariance matrix; desensitised Kalman filter; nonlinear systems; parameter uncertainties; robust unscented Kalman filter; state estimation problems; weight matrix; Covariance matrices; Equations; Estimation; Kalman filters; Mathematical model; Robustness; Uncertain systems; Nonlinear filtering; Robust filtering; State Estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE Annual Conference (SICE), 2014 Proceedings of the

Conference_Location

Sapporo

Type

conf

DOI

10.1109/SICE.2014.6935312

Filename

6935312

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=133169