Advertisement

Micro-Doppler Classification for Ground Surveillance Radar Using Speech Recognition Tools

  • Dalila Yessad
  • Abderrahmane Amrouche
  • Mohamed Debyeche
  • Mustapha Djeddou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

Among the applications of a radar system, target classification for ground surveillance is one of the most widely used. This paper deals with micro-Doppler Signature (μ-DS) based radar Automatic Target Recognition (ATR). The main goal for performing μ-DS classification using speech processing tools was to investigate whether automatic speech recognition (ASR) techniques are suitable methods for radar ATR. In this work, extracted features from micro-Doppler echoes signal, using MFCC, LPC and LPCC, are used to estimate models for target classification. In classification stage, two parametric models based on Gaussian Mixture Model (GMM) and Greedy GMM were successively investigated for echo target modeling. Maximum a posteriori (MAP) and Majority-voting post-processing (MV) decision schemes are applied. Thus, ASR techniques based on GMM and GMM Greedy classifiers have been successfully used to distinguish different classes of targets echoes (humans, truck, vehicle and clutter) recorded by a low-resolution ground surveillance Doppler radar. Experimental results show that MV post processing improves target recognition and the performances reach to 99,08% correct classification on the testing set.

Keywords

Automatic Target Recognition (ATR) micro-Doppler Signatures (μ-DS) Automatic Speech Recognition (ASR) Gaussian Mixture Model (GMM) Greedy GMM Maximum a Posteriori (MAP) Majority Vote (MV) 

References

  1. 1.
    Thayaparan, T., Abrol, S., Riseborough, E., Stankovic, L., Lamothe, D., Duff, D.: Analysis of radar micro-Doppler signatures from experimental helicopter and human data. IEE Proc. Radar Sonar Navigation 1(4), 288–299 (2007)Google Scholar
  2. 2.
    Natecz, M., Rytel-Andrianik, R., Wojtkiewicz, A.: Micro-Doppler analysis of signal received by FMCW radar. In: International Radar Symposium, Germany (2003)Google Scholar
  3. 3.
    Boashash, B.: Time frequency signal analysis and processing, 1st edn. Elsevier Ltd. (2003)Google Scholar
  4. 4.
    Chen, V.C., Ling, H.: Time frequency transforms for radar imaging and signal analysis. Artech House, Boston (2002)zbMATHGoogle Scholar
  5. 5.
    Anderson, M., Rogers, R.: Micro-Doppler analysis of multiple frequency continuous wave radar signatures. In: SPIE Proc. Radar Sensor Technology, vol. 654 (2007)Google Scholar
  6. 6.
    Chen, V.C.: Analysis of radar micro-Doppler signature with time-frequency transform. In: Proc. Tenth IEEE Workshop on Statistical Signal and Array Processing, pp. 463–466 (2000)Google Scholar
  7. 7.
    Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker verification using adapted Gaussian mixture models. Digit. Signal Process. 10, 19–41 (2000)CrossRefGoogle Scholar
  8. 8.
    Li, J.Q., Barron, A.R.: Mixture density estimation. In: Advances in Neural Information Processing Systems, vol. 12. MIT Press, Cambridge (2000)Google Scholar
  9. 9.
    Bilik, I., Tabrikian, J., Cohen, A.: GMM-based target classification for ground surveillance Doppler radar. IEEE Trans. on Aerospace and Electronic Systems 42(1), 267–278 (2006)CrossRefGoogle Scholar
  10. 10.
    Verbeek, J.J., Vlassis, N., Krose, B.: Efficient greedy learning of Gaussian mixture models. Neural Computation 5(2), 469–485 (2003)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dalila Yessad
    • 1
  • Abderrahmane Amrouche
    • 1
  • Mohamed Debyeche
    • 1
  • Mustapha Djeddou
    • 2
  1. 1.Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer SciencesUSTHBAlgiersAlgeria
  2. 2.Communication Systems LaboratoryEcole Militaire Polytechnique, BEBAlgiersAlgeria

Personalised recommendations