Comparative Analysis of Diverse Approaches for Air Target Classification Based on Radar Track Data

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)


Air Target Classification in a hostile scenario will be a decisive factor for threat evaluation and weapon assignment. Stealth technology denies any high frequency based regime for such classification. It is observed that kinematics of an air target is one thing that cannot be deceived. The present study makes an attempt to ascertain an appropriate Classification algorithm. On the basis of certain significant feature vectors the classifier classifies the data set of an air target into a target class. Feature vectors are derived from the Radar Track Data using Matlab code. The work presented here aims to compare the predictability importance of features using different classification algorithms.


Air Target Classification Feature Vectors Kinematics Predictability Importance Radar Track data 


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  1. 1.
    Borden, B.H.: Enhanced Range Profiles for Radar-Based Target Classification using Monopulse Tracking Statistics. IEEE Transactions on Antennas and Propagation 43(8) (August 1995)Google Scholar
  2. 2.
    Hu, R., Zhu, Z.: Researches on Radar Target Classification Based on High Resolution Range Profiles. In: Proc. of the IEEE Aerospace Conference, vol. 4, pp. 1243–1251 (2002)Google Scholar
  3. 3.
    Lanterman, A.D.: Tracking and Recognition of Airborne Targets via Commercial television and FM radio signals. In: Proc. of SPIE Acquisition, Tracking, and Pointing, vol. 3692, pp. 189–198 (1999)Google Scholar
  4. 4.
    Herman, S., Moulin, P.: A Particle Filtering Approach to FM-Band Passive Radar Tracking and Automatic Target Recognition. In: Proc. of the IEEE Aerospace Conference, vol. 4, pp. 1789–1808 (2002)Google Scholar
  5. 5.
    Cutaia, N.J., O’Sullivan, J.A.: Automatic Target Recognition using Kinematic Priors. IEEE Transactions on Aerospace and Electronics Engineering, AES-23 (May 1987)Google Scholar
  6. 6.
    Edwards, G., Tate, J.P.: Target Recognition and Classification using Neural Networks. IEEE Transactions on Antennas and Propagation 43(8), 1439–1442 (2002)Google Scholar
  7. 7.
    Kouemou, G.: Radar Target Classification Technologies. In: INTECH, Croatia, down-loaded from SCIYO.COM, p. 410 (December 2009) ISBN 978-953-307-029-2Google Scholar
  8. 8.
    Bogler, P.L.: Tracking a maneuvering Target using input estimation. IEEE Transactions on Aerospace and Electronics Engineering, AES-23, pp. 298–310 (May 1987)Google Scholar
  9. 9.
    Cutaia, N.J., O’Sullivan, J.A.: Identification of maneuvering aircraft using class de-pendent kinematic model. In: Research Monograph, ESSRL-95-13, Electronic Signals and Systems Research Laboratory, Department of Electrical Eng., Washington University, St. Louis, MO (May 1995)Google Scholar
  10. 10.
    Whitford, R.: Design for Air Combat. Janes (1987)Google Scholar
  11. 11.
    Stinton, D.: The Anatomy of the Aeroplane. BSP (1985)Google Scholar
  12. 12.
    Garg, M., Singh, U.: C & R Tree based Air Target Classification using Kinematics. In: National Conference on Research Trends in Computer Science and Technology (NCRTCST), IJCCT_Vol3Iss1/IJCCT_Paper_3 (2012)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer EngineeringDefence Institute of Advanced TechnologyPuneIndia

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