Target Recognition and Classification Techniques

Part of the Signals and Communication Technology book series (SCT)


Target recognition is increasingly becoming an important part of radar processing for automotive applications [1]. The reason for this development is that the environment in which the automotive radar operates is highly cluttered which makes it essential to distinguish targets of interest with a high degree of precision.


  1. 1.
    Heuel, S., Rohling, H.: Pedestrian recognition based on 24 GHz radar sensors. In: 11-th International Radar Symposium, 12 Aug 2010Google Scholar
  2. 2.
    Chen, V., et al.: Micro-doppler effect in radar: phenomenon, model, and simulation study. IEEE Trans. Aerosp. Electron. Syst. 42(1), 2–21 (2006)CrossRefGoogle Scholar
  3. 3.
    Bilik, I., Tabrikian, J.: Radar target classification using doppler signatures of human locomotion models. IEEE Trans. Aerosp. Electron. Syst. 43(4), 1510–1522 (2007)CrossRefGoogle Scholar
  4. 4.
    Yan, H., Doerr, W., Ioffe, A., Clasen, H.: Micro-doppler based classifying features for automotive radar VRU target classification. In: 25th International Technical Conference on the Enhanced Safety of Vehicles (ESV), Detroit Michigan, United States (2017)Google Scholar
  5. 5.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Inc., New York (1995)zbMATHGoogle Scholar
  6. 6.
  7. 7.
  8. 8.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefGoogle Scholar
  9. 9.
    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, 179–188 (1936)CrossRefGoogle Scholar
  10. 10.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer (2008)Google Scholar
  11. 11.
    Cortes, C., Vapnik, V.: Support-vector network. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  12. 12.
    Huang, Z., Lee, B.G.: Combining non-parametric models for multisource predictive forest mapping. Photogram. Eng. Remote Sens. 70, 415–425 (2004)CrossRefGoogle Scholar
  13. 13.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM (1992)Google Scholar
  14. 14.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  15. 15.
    Camps-Valls, G., Bruzzone, L.: Kernel-based methods for Hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 43(6), 1351–1362 (2005)CrossRefGoogle Scholar
  16. 16.
    Bruzzone, L., Persello, C.: A novel context-sensitive semi-supervised SVM classifier robust to mislabeled training samples. IEEE Trans. Geosci. Remote Sens. 47(7) (2009)CrossRefGoogle Scholar
  17. 17.
    Liu, J., et al.: Radar target classification using support vector machine and subspace methods. IET Radar Sonar Navig. 9(6), 632–640 (2015)CrossRefGoogle Scholar
  18. 18.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  19. 19.
    Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition, pp. 1–43. Kluwer Academic Publishers, Boston (1998)Google Scholar
  20. 20.
    Fioranelli, F., Ritchie, M., Griffiths, H.: Bistatic human micro doppler signatures for classification of indoor activities. In: 2017 IEEE Radar Conference (Radar Conf), Seattle, WA, USA, 08–12 May 2017Google Scholar
  21. 21.
    Bloecher, H.-L., Andres, M., Fischer, C., Sailer, A., Goppelt, M., Dickmann, J.: Impact of system parameter selection on radar sensor performance in automotive applications. Adv. Radio Sci. 10, 33–37 (2012)CrossRefGoogle Scholar
  22. 22.
    Singh, J., Ginsburg, B., Rao, S., Ramasubramanian, K.: AWR 1642 mm wave sensor: 76–81-GHz radar-on-chip for short-range radar applications. Texas Instruments, May 2017Google Scholar
  23. 23.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Hinton, G.E., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18 (2006)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Furukawa, H.: Deep learning for end-to-end automatic target recognition from synthetic aperture radar imagery. IEICE Technical Report, vol. 117, No. 403, SANE 2017-92, pp. 35–40 (2018)Google Scholar
  26. 26.
    Angelov, A., Robertson, A., Murray-Smith, R., Fioranelli, F.: Practical classification of different moving targets using automotive radar and deep neural networks. IET Radar, Sonar Navig. 12(10), 1082–1089 (2018)Google Scholar
  27. 27.
    Wheeler, T.A., Holder, M.F., Winner, H., Kochenderfer, M.J.: Deep stochastic radar models. In: IEEE Intelligent Vehicles Symposium (IV) (2017)Google Scholar
  28. 28.
    Salakhutdinov, R., Hinton, G.E.: Deep Boltzmann Machines. AISTATS, pp. 448–455 (2009)Google Scholar
  29. 29.
    Graves, A., Mohamed, A., Hinton, G.E.: Speech recognition with deep recurrent neural networks. In: ICASSP, pp. 6645–6649 (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.TsukubaJapan

Personalised recommendations