Modified unscented Kalman Filter for nonlinear systems having linear subsystems

BABACAN KOKSAL, Esin; DOROSLOVACKI, Milos Milos, Levent Levent
2015 Communications Faculty Of Science University of Ankara Series A1Mathematics and Statistics  
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
more » ... 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. Recently, a relatively new nonlinear filtering algorithm named Unscented Kalman Filter (UKF) is proposed as an improvement to EKF [3] . UKF is based on the unscented transformation, which uses a set of appropriately chosen weighted sigma points to estimate the means and covariances of probability distributions. It is not necessary to calculate Jacobians and so the algorithm has superior implementation properties to the EKF [4]. The UKF is widely used in practice: target tracking [3], position determination [5], multi-sensor fusion [6] and training of neural networks [7]. ESIN KOKSAL BABACAN, MILOS I. DOROSLOVACKI AND LEVENT ÖZBEK 90
doi:10.1501/commua1_0000000736 fatcat:3dkieq4z4fezzdvprzlko24lrq