A simplified αβ based Gaussian sum filter

Authors: Meche Abdelkrim, Dahmani Mohammed, Keche Mokhtar, Ouamri Abdelaziz

Abstract:
State estimation is a major problem in many fields, such as target tracking. For a linear Gaussian dynamic system, the KF provides the optimal state estimate, in the minimum mean square error sense. In general, however, real-world systems are governed by the presence of non-Gaussian noise and/or nonlinear systems. In this paper, the problem of state estimation in the case of a linear system affected by a nonGaussian measurement noise is addressed. Based on the theoretical framework of the Gaussian sum filters (GSF), we propose a novel static version of this filter that uses the well known α β filter. The simulation results show that the proposed filter has acceptable performances in terms of RMSE and a reduced computational load, compared to the classical GSF.

Keywords:
State estimation
Kalman filter
α β filter
Gaussian sum filter
Non-Gaussian noise

Published in: AEÜ-International Journal of Electronics and Communications (Volume 67, Issue 4, April 2013)

Publisher: Elsevier

ISSN Information: 1434-8411

A simplified αβ based Gaussian sum filter

Bình luận của bạn

*

*

*

*

 Captcha

Gửi bình luận nhập lại