Abstract
This paper is focused on the Automatic Target Recognition (ATR) using Support Vector Machines (SVM) combined with automatic speech recognition (ASR) techniques. The problem of performing recognition can be broken into three stages: data acquisition, feature extraction and classification. In this work, extracted features from micro-Doppler echoes signal, using MFCC, LPCC and LPC, are used to estimate models for target classification. In classification stage, three parametric models based on SVM, 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 SVM, 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. The obtained performances show a high rate correct classification on the testing set.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Natecz, M., Rytel-Andrianik, R., Wojtkiewicz, A.: Micro-Doppler Analysis of Signal Received by FMCW Radar. In: International Radar Symposium, Germany (2003)
Boashash, B.: Time Frequency Signal Analysis and Processing a comprehensive reference, 1st edn. Elsevier Ltd. (2003)
Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)
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)
Chen, V.C., Ling, H.: Time Frequency Transforms for Radar Imaging and Signal Analysis. Artech House, Boston (2002)
Anderson, M., Rogers, R.: Micro-Doppler Analysis of Multiple Frequency Continuous Wave Radar Signatures. In: SPIE Proc. Radar Sensor Technology, vol. 654 (2007)
Thayaparan, T., Abrol, S., Riseborough, E., Stankovic, L., Lamothe, D., Duff, G.: Analysis of Radar Micro-Doppler Signatures From Experimental Helicopter and Human Data. IEE Proc. Radar Sonar Navigation 1(4), 288–299 (2007)
Reynolds, D.A.A.: Gaussian Mixture Modeling Approach to Text-Independent Speaker Identification. Ph.D.dissertation, Georgia Institute of Technology, Atlanta (1992)
Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker Verification Using Adapted Gaussian Mixture Models. Digit. Signal Process. 10, 19–41 (2000)
Campbell, J.P.: Speaker Recognition: a tutorial. Proc.of the IEEE 85(9), 1437–1462 (1997)
Li, J.Q., Barron, A.R.: Mixture Density Estimation. In: Advances in Neural Information Processing Systems, p. 12. MIT Press, Cambridge (2002)
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)
Vander, H.F., Duin, W.R.P., de Ridder, D., Tax, D.M.J.: Classification, Parameter Estimation and State Estimation. John Wiley & Son, Ltd. (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yessad, D., Amrouche, A., Debyeche, M. (2011). SVM and Greedy GMM Applied on Target Identification. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-24958-7_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24957-0
Online ISBN: 978-3-642-24958-7
eBook Packages: Computer ScienceComputer Science (R0)