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Tytuł artykułu

The analysis of methods of interaction between elementary filters in multiple model tracking filter in marine radars

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Języki publikacji
EN
Abstrakty
EN
Radar target tracking on a sea-going ship is a basic source of information about movement of other vessels, influencing directly safety of navigation. The research on improving of tracking methods has led to a new concept of multiple model neural filtration, which is a combination of multiple model approach with the use of artificial neural networks for the needs of estimation of movement vector. One of the key issue during designing of filter id to establish rules of interaction between elementary filters. The paper presents the most popular methods of manoeuvre detection and interaction of elementary filters in the numerical filtration. The modifications of them for the needs of neural tracking are proposed. Additionally, a concept of use of probabilistic neural network for this purpose is described. The idea was checked in the experimental research with the use of simulation. The result of the research confirmed usefulness of using PNN in multiple model filtration, showing however simultaneously the directions of future research in this. The research was financed by Polish National Centre of Science under the research project “Development of radar target tracking methods of floating targets with the use of multiple model neural filtering”.
Rocznik
Strony
81--87
Opis fizyczny
Bibliogr. 12 poz., rys., tab.
Twórcy
  • Maritime University of Szczecin, Faculty of Navigation, Chair of Geoinformatics 70-500 Szczecin, ul. Wały Chrobrego 1–2, w.kazimierski@am.szczecin.pl
Bibliografia
  • 1. MAGILL D.T.: Optimal Adaptive Estimation of Sampled Stochastic Processes. IEEE Trans.
  • 2. Automatic Control, AC-10:434–439, 1965.
  • 3. BAR SHALOM Y., LI X.R., KIRUBARJAN T.: Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. John Wiley & Sons, Inc., NY USA, 2001.
  • 4. LI X.R, JILKOV V.P.: A Survey of Manoeuvring Target Tracking. Part V: Multiple-Model Methods. IEEE Transactions on Aerospace and Electronic Eystems, Vol. 41, 2005.
  • 5. KAZIMIERSKI W.: Determining of marine radar target movement models for the needs of multiple model neural tracking filetr. Deutsche Gesellschaft für Ortung und Navigation, e.V. Inaternational Radar Symposium IRS 2011, Leipzig, Niemcy 07–09.09.2011, 611–616.
  • 6. WOŁEJSZA P.: Statistical analysis of real radar target course and speed changes for the needs of multiple model tracking filter. Scientific Journals Maritime University of Szczecin 30(102), 2012, 166–169.
  • 7. LI X.R., JILKOV V.P.: A Survey of Manoeuvring Target Tracking. Part V: Multiple-Model Methods. IEEE Transactions on Aerospace and Electronic Eystems, Vol. 41, 2005.
  • 8. JUSZKIEWICZ W., STATECZNY A.: GRNN Cascade Neural Filter for Tracked Target Manoeuvre Estimation. Neural Networks and Soft Computing, Zakopane 2000.
  • 9. KAZIMIERSKI W.: Two-stage General Regression Neural network for radar target tracking. Polish Journal of Environmental Studies, Vol. 17, No. 3B, 2008.
  • 10. STATECZNY A. (ed.): Radar navigation. GTN, Gdańsk 2011 (in Polish).
  • 11. LI X.R, JILKOV V.P.: A Survey of Manoeuvring Target Tracking. Part IV: Decision-Based Methods, Proc. of SPIE Conference on Signal and Data Processing of Small Targets, Orlando USA, 2002.
  • 12. SPECHT D.F.: Probabilistic Neural Networks. Neural Networks, Vol. 3, 1990, 109–118.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWM7-0007-0036
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