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Software implementation of multiple model neural filter for radar target tracking

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents a software implementation of multiple model neural filter for radar target tracking. Such a filter may be proposed as an interesting alternative for numerical filters. The main purpose of software implementation is to provide a tool for complex research of the filter possibilities and adjusting options. A concept of a filter is briefly mentioned, however the main body of paper is focused on user-approach detailed description of application with UML use-case diagrams. Examples of detailed presentation of usecases are given and the general use-case diagram for application is included. The application itself is to be an advanced tool for researchers interested in analyzing target tracking process, providing different tracking methods and the possibility of adjusting their parameters. The possibility of simulating any scenario, as well as working with real data (also on-line) was ensured. 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
88--93
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
Bibliografia
  • 1. IMO Resolution MSC.192(79), Adoption of the revised performance standards for radar equipment, 2005.
  • 2. BOLE A.G., DINELEY W.O., WALL A.: Radar and ARPA Manual. Elsevier Science & Technology Book, 2005.
  • 3. BLACKMAN S., POPOLI R.: Design and Analysis of Modern Tracking Systems. Artech House, Norwood USA, 1999.
  • 4. BAR SHALOM Y., LI X.R.: Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. John Wiley & Sons, Inc., NY USA, 2001.
  • 5. 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.
  • 6. BAR SHALOM Y., LI X.R.: Estimation and tracking: principles, techniques, and software. YBS, Norwood 1998.
  • 7. STATECZNY A. (ed.): Radar navigation. GTN, Gdańsk 2011 (in Polish).
  • 8. KAZIMIERSKI W.: Two-stage General Regression Neural Network for radar target tracking. Polish Journal of Environmental Studies, Vol. 17, No. 3B.
  • 9. SPECHT D.F.: A General Regression Neural Network. IEEE Transactions on Neural Network, Vol. 2, No. 6, 1991.
  • 10. STATECZNY A., KAZIMIERSKI W.: The Process of Radar Tracking by Means of GRNN Artificial Neural Network with Dynamically Adapted Teaching Sequence Length in Algorithmic Depiction. Proceedeings of 7th International Symposium of Navigation TrnasNav2007, Gdynia 2007.
  • 11. STEMPOSZ E., PŁODZIEŃ J.: Analiza i projektowanie systemów informatycznych. Wydawnictwo PJWSTK, Warszawa 2003.
  • 12. MISER H.J., QUADE E.S.: Handbook of System Analysis. Wiley 1985.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWM7-0007-0037
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