Methods of position estimation in parametric navigation

• Autorzy: Banachowicz A. , Wolski A.

Identyfikatory

DOI

10.20858/sjsutst.2017.95.2

• Języki publikacji: EN

Abstrakty

EN

The estimation of position coordinates of a navigating ship is one of the navigational subprocesses. The methods used in this process are either deterministic (the case of a minimum number of navigational parameters measurements) or probabilistic (in cases where we have access to information redundancy). Naturally, due to the accuracy and reliability of the calculated coordinates, probabilistic methods should be primarily used. The article presents the use of the method of least squares and Kalman filtering in algorithms in integrated navigation for the estimation of position coordinates, taking into account ship movement parameters.

Słowa kluczowe

EN

navigational data fusion; least squares estimation; Kalman filtering; estimation; navigation; algorithm of integrated navigation

PL

fuzja danych nawigacyjnych; estymacja najmniejszych kwadratów; filtrowanie Kalmana; oszacowanie; nawigacja; algorytm zintegrowanej nawigacji

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Transport / Politechnika Śląska
• Rocznik: 2017
• Tom: z. 95
• Strony: 19--25
• Opis fizyczny: Bibliogr. 10 poz.

Twórcy

autor

Banachowicz A.

• Department of Artificial Intelligence and Applied Mathematics, West Pomeranian University of Technology, Żołnierska 49 Street, 71-210 Szczecin, Poland

autor

Wolski A.

• Polish Naval Academy, Śmidowicza 69 Street, 81-127 Gdynia, Poland

Bibliografia

• 1. Anderson B.D.O., J.B. Moore. 1979. Optimal Filtering. Upper Saddle River, NJ: Prentice Hall.
• 2. Balakrishnan A.V. 1984. Kalman Filtering Theory. New York, NY: Optimization Software.
• 3. Farrell J.A., M. Barth.1999. The Global Positioning System and Inertial Navigation. New York, NY: McGraw-Hill.
• 4. Julier S.J., J.K. Uhlmann, H.F. Durrant-Whyte. 1995. “A new approach for filtering nonlinear systems”. In: The Proceedings of the American Control Conference. Seattle, Washington, 1628-1632.
• 5. Kalman R.E. 1960. “A new approach to linear filtering and prediction problems. ASME”. Journal of Basic Engineering, Series D: 82.
• 6. Kalman R.E., R.S. Bucy. 1961. “A new approach to linear filtering and prediction theory. ASME”. Journal of Basic Engineering, Series D: 83.
• 7. Mitchell H.B. 2007. Multi-sensor Data Fusion. An Introduction. Berlin-Heidelberg: Springer Verlag.
• 8. Ralston A. 1965. A First Course in Numerical Analysis. New York, NY: McGraw-Hill.
• 9. Rao C.R. 1973. Linear Statistical Inference and its Applications, Second Edition. New York, NY: John Wiley & Sons.
• 10. Ristic B., S. Arulampalm, N. Gordon. 2004. Beyond the Kalman Filter. Participle Filters for Tracking Applications. Boston, MA: Artech House.