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Author Zalewski, Paweł
Affiliation Maritime University of Szczecin, Institute of Marine Traffic Engineering 70-500 Szczecin, ul. Wały Chrobrego 1–2
E-mail p.zalewski@am.szczecin.pl
Author Tomczak, Arkadiusz
Affiliation Maritime University of Szczecin, Institute of Marine Traffic Engineering 70-500 Szczecin, ul. Wały Chrobrego 1–2
E-mail a.tomczak@am.szczecin.pl
ISSN printed 1733-8670
URI https://repository.am.szczecin.pl/handle/123456789/62
Abstract PNDS (Pilot Navigation and Docking System) [1] utilizes the range measurement between laser head and ship’s side to determine the ship’s outline presented on the screen. The noisy measurements and dynamic process noise affect the accuracy of determined parameters and propagate to ship’s heading and position. To improve the performance of PNDS system there was a need to apply the data filtering technique. In the paper the theoretical basis and algorithm of discrete Kalman filter designed for range optimal estimation in PNDS were described. The real data collected during berthing operation of motor vessel Navigator XXI were filtered and compared to row unfiltered data. The conclusions contain evaluation of filter capabilities and its potential application in PNDS system
Pages 182–185
Publisher Scientific Journals of the Maritime University of Szczecin, Zeszyty Naukowe Akademii Morskiej w Szczecinie
Keywords Kalman filtration
Keywords state vector estimation
Keywords range measurement
Keywords laser range
Keywords docking system
Title Laser range measurement filtration for PNDS purposes
Type Original scientific article
References
  1. BĄK A.: Zintegrowany system wizualizacji parametrów nawigacyjnych w PNDS. Proceedings of 14th International Scientific and Technical Conference on Marine Traffic Engineering, Edited by L. Gucma, Świnoujście, 12–14 Oct., Maritime University of Szczecin, 2011.
  2. KALMAN R.: A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME, Journal of Basing Engineering, vol. 82, March 1960, 34–35.
  3. SIMONS D.: Kalman Filtering with State Constraints: A Survey of Linear and Nonlinear Algorithms. Cleveland State University Department of Electrical and Computer Engineering, IET Control Theory & Applications, 2009.
  4. WELCH G., BISHOP G.: An Introduction to the Kalman Filter. Transactions 95-041, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599- 3175, 2002.
  5. ZALEWSKI P.: Modele z filtrem Kalmana i rozmyte w systemach dynamicznego pozycjonowania. I Międzynarodowa Konferencja Naukowo-Techniczna „Górnictwo morskie surowcową szansą przyszłych pokoleń”, AGH, Górnictwo i Geoinżynieria, rok 35, zeszyt 4/1, Kraków 2011.
ISSN on-line 2392-0378
Language English
Funding No data
Figures 4
Tables 0
Published 2012-12-10
Accepted 2012-11-06
Recieved 2012-10-02


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