Tracking Aircrafts by Using Impulse Exclusive Filter with RBF Neural Networks

  • Pınar Çivicioğlu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3949)


Target Tracking based on Artificial Neural Networks has become a very important research field in Dynamic Signal Processing. In this paper, a new Target Tracking filter, entitled RBF neural network based Target Tracking Filter, RBF-TT, has been proposed. The tracking performance of the proposed filter, RBF-TT, has also been compared with the classical Kalman Filter based Target Tracking algorithm. Predictions during experiments have been made for the civil aircraft positions, one step ahead in real time. Extensive simulations revealed that the proposed filter supplies superior tracking performances to the Kalman Filter based comparison filter.


Kalman Filter Target Track Impulsive Noise Civil Aviation Radar Sensor 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House, USA (1999)Google Scholar
  2. 2.
    Bar-Shalom, Y., Blair, W.D.: Multitarget-Multisensor Tracking: Applications and Advances Volume III. Artech House, Inc., USA (2000)Google Scholar
  3. 3.
    Bar-Shalom, Y., Li, X., Kırubarajan, T.: Estimation with Applications to Tracking and Navigation. Theory Algorithms and Software. John Wiley & Sons, Inc., USA (2001)CrossRefGoogle Scholar
  4. 4.
    Bierman, G.: Vector Neural Network Signal Integration for Radar Application. Signal and Data Processing of Small Targets 2235, 290–302 (2001)Google Scholar
  5. 5.
    Shams, S.: Neural Network Optimization for Multi-Target Multi-Sensor Passive Tracking. Proc. IEEE 84(10), 1442–1457 (1996)CrossRefGoogle Scholar
  6. 6.
    Haykın, S.: Neural networks. Macmillan, New York (1994)MATHGoogle Scholar
  7. 7.
    Li, X.R., Jilkov, W.P.: Survey of Maneuvering Target Tracking-Part I: Dynamic Models. IEEE Transactions on Aerospace and Electronic Systems 39(4), 1333–1364 (2003)CrossRefGoogle Scholar
  8. 8.
    Li, N., Li, X.R.: Target Perceivability and its Applications. IEEE Transactions on Signal Processing 49(11), 2588–2604 (2001)CrossRefGoogle Scholar
  9. 9.
    Wang, X., Challa, S., Evans, R., Li, X.R.: Minimal Sub-Model-Set Algorithm for Maneuvering Target Tracking. IEEE Transactions on Aerospace and Electronic Systems 39(4) (2003)Google Scholar
  10. 10.
    Chen, H., Kirubarajan, T., Bar-Shalom, Y., Pattipati, K.R.: An MDL Approach for Multiple Low Observable Track Initiation. IEEE Trans. Aerospace and Electronic Systems 39(3), 862–882 (2003)CrossRefGoogle Scholar
  11. 11.
    Tartakovsky, A.G., Li, X.R., Yaralov, G.: Sequential Detection of Targets in Multichannel Systems. IEEE Transactions on Information Theory 49(2), 425–445 (2003)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Brookner, E.: Tracking and Kalman Made Easy, pp. 60–62. John Wiley & Sons, Chichester (1998)CrossRefGoogle Scholar
  13. 13.
    Mahafza, B.R.: Radar Systems Analysis and Design Using Matlab. Chapman & Hall/CRC, USA (2000)CrossRefMATHGoogle Scholar
  14. 14.
    Civil Aviation Safety Authority (CASA), Civil Aircraft Register,
  15. 15.
    Li, N., Li, X.R.: Tracker Design based on Target Perceivability. IEEE Transactions on Aerospace and Electronic Systems 37(1), 214–225 (2001)CrossRefGoogle Scholar
  16. 16.
    Çiviciog̃lu, P., Alçı, M.: Impulsive Noise Suppression from Highly Distorted Images with Triangular Interpolants. AEU International Journal of Electronics and Communications 58(5), 311–318 (2004)CrossRefGoogle Scholar
  17. 17.
    Çiviciog̃lu, P., Alçı, M.: Edge Detection of Highly Distorted Images Suffering from Impulsive Noise. AEU International Journal of Electronics and Communications 58(6), 413–419 (2004)CrossRefGoogle Scholar
  18. 18.
    Çiviciog̃lu, P., Alçı, M., Beşdok, E.: Using an Exact Radial Basis Function Artificial Neural Network for Impulsive Noise Suppression from Highly Distorted Image Databases. In: Yakhno, T. (ed.) ADVIS 2004. LNCS, vol. 3261, pp. 383–391. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    MathWorks, Neural Networks Toolbox, MATLAB v7.00, Function Reference, New York, The MathWorks, Inc. (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pınar Çivicioğlu
    • 1
  1. 1.Civil Aviation School, Department of Aircraft Electrics and ElectronicsErciyes UniversityKayseriTurkey

Personalised recommendations