Position estimation using Unscented Kalman Filter

• Autorzy: Konatowski S. , Sipa T.
• Języki publikacji: EN

Abstrakty

EN

Position estimation in integrated navigation systems often calls for operations on nonlinear system models. Dynamics nonlinearity of an object, which position we want to estimate requires using special filters. The Extended Kalman Filter based on linearization of nonlinear functions is generally accepted solution. The paper presents the Unscented Kalman Filter based on Unscented Transform. Filter performance with comparison to extended Kalman filter is discussed on the theoretical base and simulation results showing accuracy increase are presented.

Słowa kluczowe

EN

Kalman filter; integrated navigation system

PL

filtr Kalmana; zintegrowany system nawigacyjny

• Wydawca: Polskie Forum Nawigacyjne
• Czasopismo: Annual of Navigation
• Rocznik: 2004
• Tom: No. 8
• Strony: 97--110
• Opis fizyczny: Bibliogr. 8 poz., rys.

Twórcy

autor

Konatowski S.

• Military University of Technology, Warszawa

autor

Sipa T.

• Military University of Technology, Warszawa

Bibliografia

• 1. A. Doucet, N. de Freitas, R. Van der Merwe, E.A.Wan: The unscented particle filter; Cambridge University Engineering Department, Cambridge, 2000.
• 2. S.J. Julier, J.K. Uhlmann: a new extension of the Kalman filter to nonlinear systems; Proceedings of Aero Sense: The 11-th International Symposium on Aerospace/Defense Sensing, Simulations and Controls, 1997.
• 3. J. Lampinen, S. Särkkä, T. Tamminen, A. Vehtari: Probabilistic Methods in Multiple Target Tracking – Review and Bibliography; Laboratory of Computational Engineering Helsinki University of Technology, Helsinki, 2004.
• 4. J. Stephen: Development of a multi – sensor GNSS based vehicle navigation; University of Calgary, Calgary, 2000.
• 5. R. Van der Merwe, E.A.Wan: The square-root unscented Kalman filter for state and parameter-estimation; Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Salt Lake City, 2001.
• 6. R. Van der Merwe, E.A.Wan: The Unscented Kalman Filter; Department of Electrical and Computer Engineering, Oregon Graduate Institute of Science and Technology, Beaverton, Oregon, 2001.
• 7. N.J. Gordon, B. Ristic, S. Arulampalam: Beyond the Kalman Filter – particle filters for tracking applications, 2004.
• 8. N.J. Gordon, D.J. Salmond, A.F.M. Smith: Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings-F 140(2), 1993, pp 107-113.