MODIFIED UNSCENTED KALMAN FILTER FOR NONLINEAR SYSTEMS

Year 2015, Volume: 64 Issue: 2, 89 - 98, 01.08.2015

Esin Babacan Koksal İ. Doroslovackı Milos Levent Özbek

https://doi.org/10.1501/Commua1_0000000736

Abstract

The Extended Kalman Filter (EKF) is the often used filtering algorithm for nonlinear systems. But it does not usually produce desirable results. Recently a new nonlinear filtering algorithm named as Unscented Kalman Filter (UKF) is introduced. In this paper, we propose a new modified Unscented Kalman Filter (MUKF) algorithm for nonlinear stochastic systems that are linear in some components. These nonlinear systems can be considered as having linear subsystems with parameters and aim is to estimate the system parameters. In simulation study, performance of the EKF, its known variant Modified Extended Kalman Filter (MEKF), UKF and the proposed MUKF is demonstrated for a nonlinear system that is linear in some components. The results show that MUKF gives the best solution for parameter identification problem

Keywords

Nonlinear Dynamic System, System idendification, Extended Kalman Filter, Unscented Kalman Filter.

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• Current Address: Esin Köksal Babacan, Ankara University, Department of Statistics, Faculty of Science, 06100 Tandoğan, Ankara-TURKEY