MODIFIED UNSCENTED KALMAN FILTER FOR NONLINEAR SYSTEMS HAVING LINEAR SUBSYSTEMS

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Atıf İçin Kopyala

Babacan E., Doroslovacki M. I., Ozbek L.

COMMUNICATIONS FACULTY OF SCIENCES UNIVERSITY OF ANKARA-SERIES A1 MATHEMATICS AND STATISTICS, cilt.64, sa.2, ss.89-98, 2015 (ESCI)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 64 Sayı: 2
• Basım Tarihi: 2015
• Doi Numarası: 10.1501/commua1-2_0000000736
• Dergi Adı: COMMUNICATIONS FACULTY OF SCIENCES UNIVERSITY OF ANKARA-SERIES A1 MATHEMATICS AND STATISTICS
• Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), TR DİZİN (ULAKBİM)
• Sayfa Sayıları: ss.89-98
• Anahtar Kelimeler: Nonlinear Dynamic System, System idendification, Extended Kalman Filter, Unscented Kalman Filter
• Ankara Üniversitesi Adresli: Evet

Özet

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.