Convergence analysis of cubature Kalman filter

Author

Zarei, Jafar ; Shokri, Ehsan ; Karimi, Hamid Reza

Author_Institution

Dept. of Control Eng., Shiraz Univ. of Technol., Shiraz, Iran

fYear

2014

fDate

24-27 June 2014

Firstpage

1367

Lastpage

1372

Abstract

This paper investigates the stability analysis of cubature Kalman filter (CKF) for nonlinear systems with linear measurement. The certain conditions to ensure that the estimation error of CKF remains bounded are proved. Then, the effect of process noise covariance is investigated and an adaptive process noise covariance is proposed to deal with large estimation error. Accordingly, a modified CKF (MCKF) is developed to enhance the stability and accuracy of state estimation. The performance of the MCKF is compared to the CKF by two case studies. Simulation results demonstrate that the large estimation error may lead to instability of CKF while the MCKF is successfully able to estimate the states.

Keywords

Kalman filters; convergence; covariance analysis; nonlinear filters; nonlinear systems; stability; state estimation; CKF estimation error; MCKF performance; adaptive process noise covariance effect; convergence analysis; cubature Kalman filter; modified CKF; nonlinear filtering; nonlinear systems; stability analysis; state estimation; Accuracy; Convergence; Estimation error; Stability analysis; State estimation; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 2014 European

Conference_Location

Strasbourg

Print_ISBN

978-3-9524269-1-3

Type

conf

DOI

10.1109/ECC.2014.6862199

Filename

6862199