Analysis of the Possibility of Using Radar Tracking Method Based on GRNN for Processing Sonar Spatial Data

  • Witold Kazimierski
  • Grzegorz Zaniewicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8537)


This paper presents the approach of applying radar tracking methods for tracking underwater objects using stationary sonar. Authors introduce existing in navigation methods of target tracking with particular attention to methods based on neural filters. Their specific implementation for sonar spatial data is also described. The results of conducted experiments with the use of real sonograms are presented.


sonar target tracking geo-data spatial data processing 


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  1. 1.
    Pietrzykowski, Z., Borkowski, P., Wołejsza, P.: Marine integrated navigational decision support system. In: Mikulski, J. (ed.) TST 2012. CCIS, vol. 329, pp. 284–292. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Maleika, W.: The influence of track configuration and multibeam echosounder parameters on the accuracy of seabed DTMs obtained in shallow water. Earth Science Informatics 6(2), 47–69 (2013)CrossRefGoogle Scholar
  3. 3.
    Maleika, W., Palczynski, M., Frejlichowski, D.: Interpolation Methods and the Accuracy of Bathymetric Seabed Models Based on Multibeam Echosounder Data. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ACIIDS 2012, Part III. LNCS (LNAI), vol. 7198, pp. 466–475. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Borkowski, P.: Ship course stabilization by feedback linearization with adaptive object model. Polish Maritime Research 211(81), 14–19 (2014)Google Scholar
  5. 5.
    Lekkerkerk, H.-J., Theijs, M.J.: Handbook of offshore surveying. Skilltrade (2011)Google Scholar
  6. 6.
    Modalavalasa, N., SasiBhushana Rao, G., Satya Prasad, K.: An Efficient Implementation of Tracking Using Kalman Filter for Underwater Robot Application. IJCSIT 2(2) (2012)Google Scholar
  7. 7.
    Velastin, S.A., Remagnino, P. (eds.): Intelligent distributed video surveillance systems. IET, London (2006)Google Scholar
  8. 8.
    Bar Shalom, Y., Li, X.R.: Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. John Wiley & Sons, Inc., NY (2001)Google Scholar
  9. 9.
    Wood, T.: Mathematical Modelling Of Single Target SONAR and RADAR Contact Tracking, PhD thesis. University of Oxford, Michaelmas Term (2008)Google Scholar
  10. 10.
    Hue, C., Le Cadre, J.P., Perez, P.: Tracking multiple objects with particle filtering. IEEE Trans. on Aerospace and Electronic Systems 38(3), 791–812 (2002)CrossRefGoogle Scholar
  11. 11.
    Clark, D.E., Bell, J., de Saint-Pern, Y., Petillot, Y.: PHD filter multi-target tracking in 3D sonar. In: Oceans 2005 – Europe, vol. 1 (2005)Google Scholar
  12. 12.
    Lane, D.M., Chantler, M.J., Dai, D.: Robust Tracking of Multiple Objects in Sector-Scan Sonar Image Sequences Using Optical Flow Motion Estimation. IEEE Journal of Oceanic Engineering 23 (1998)CrossRefGoogle Scholar
  13. 13.
    Stateczny, A., Kazimierski, W.: A comparison of the target tracking in marine navigational radars by means of GRNN filter and numerical filter. In: 2008 IEEE Radar Conference, Rome. IEEE Radar Conference, vol. 1-4, pp. 1994–1997 (2008)Google Scholar
  14. 14.
    Stateczny, A.: Artificial neural networks for comparative navigation. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 1187–1192. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Stateczny, A.: Neural manoeuvre detection of the tracked target in ARPA systems. In: Katebi, R. (ed.) Control Applications in Marine Systems 2001 (CAMS 2001), Glasgow. IFAC Proceedings Series, pp. 209–214 (2002)CrossRefGoogle Scholar
  16. 16.
    Stateczny, A.: The neural method of sea bottom shape modelling for the spatial maritime information system. In: Brebbia, C., Olivella, J. (eds.) Maritime Engineering and Ports II, Barcelona. Water Studies Series, vol. 9, pp. 251–259 (2000)Google Scholar
  17. 17.
    Lubczonek, J.: Hybrid neural model of the sea bottom surface. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 1154–1160. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Lubczonek, J., Stateczny, A.: Concept of neural model of the sea bottom surface. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing. AISC, vol. 19, pp. 861–866. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  19. 19.
    Przyborski, M., Pyrchla, J.: Reliability of the navigational data. In: Klopotek, M.A., Wierzchon, S.T. (eds.) IIS: IIPWM 2003. AISC, vol. 22, pp. 541–545. Springer, Heidelberg (2003)Google Scholar
  20. 20.
    Stateczny, A., Kazimierski, W.: Selection of GRNN network parameters for the needs of state vector estimation of manoeuvring target in ARPA devices. In: Romaniuk, R.S. (ed.) Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments IV. Proceedings of (SPIE), vol. 6159, p. F1591. Wilga (2006)Google Scholar
  21. 21.
    Kazimierski, W.: Two – stage General Regression Neural Network for radar target tracking. Polish Journal of Environmental Studies 17(3B) (2008)Google Scholar
  22. 22.
    Stateczny, A., Kazimierski, W.: Determining Manoeuvre Detection Threshold of GRNN Filter in the Process of Tracking in Marine Navigational Radars. In: Kawalec, A., Kaniewski, P. (eds.) Proceedings of IRS 2008, Wrocław, pp. 242–245 (2008)Google Scholar

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Witold Kazimierski
    • 1
  • Grzegorz Zaniewicz
    • 1
  1. 1.Institute of GeoinformaticsMaritime University of SzczecinPoland

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