Pulse Doppler Radar Target Recognition using a Two-Stage SVM Procedure

It is possible to detect and classify moving and stationary targets using ground surveillance pulse-Doppler radars (PDRs). A two-stage support vector machine (SVM) based target classification scheme is described here. The first stage tries to estimate the most descriptive temporal segment of the radar echo signal and the target signal is classified using the selected temporal segment in the second stage. Mel-frequency cepstral coefficients of radar echo signals are used as feature vectors in both stages. The proposed system is compared with the covariance and Gaussian mixture model (GMM) based classifiers. The effects of the window duration and number of feature parameters over classification performance are also investigated. Experimental results are presented.

[1]  Christoph Neumann,et al.  Sound and dynamics of targets — Fusion technologies in radar target classification , 2008, 2008 11th International Conference on Information Fusion.

[2]  张国亮,et al.  Comparison of Different Implementations of MFCC , 2001 .

[3]  Zheng Fang,et al.  Comparison of different implementations of MFCC , 2001 .

[4]  W. Förstner,et al.  A Metric for Covariance Matrices , 2003 .

[5]  A. Cohen,et al.  GMM-based target classification for ground surveillance Doppler radar , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Douglas A. Reynolds,et al.  Robust text-independent speaker identification using Gaussian mixture speaker models , 1995, IEEE Trans. Speech Audio Process..

[7]  Min-Jea Tahk,et al.  IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS , 2022, IEEE Aerospace and Electronic Systems Magazine.

[8]  M. Jahangir,et al.  A robust Doppler classification technique based on hidden Markov models , 2002, RADAR 2002.

[9]  Vincent Wan,et al.  Speaker verification using support vector machines , 2003 .

[10]  Ulrich H.-G. Kreßel,et al.  Pairwise classification and support vector machines , 1999 .

[11]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[12]  Milenko S. Andric,et al.  The database of radar echoes from various targets with spectral analysis , 2010, 10th Symposium on Neural Network Applications in Electrical Engineering.

[13]  A. G. Stove,et al.  A Doppler-based automatic target classifier for a battlefield surveillance radar , 2002, RADAR 2002.

[14]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[15]  A. Enis Çetin,et al.  Subband analysis for robust speech recognition in the presence of car noise , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[17]  A. Enis Çetin,et al.  Image Description Using a Multiplier-Less Operator , 2009, IEEE Signal Processing Letters.

[18]  A. Enis Çetin,et al.  Teager energy based feature parameters for speech recognition in car noise , 1999, IEEE Signal Processing Letters.

[19]  A. Enis Çetin,et al.  Detection of insect damaged wheat kernels by impact acoustics , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[20]  E. J. Hughes,et al.  The Application of Speech Recognition Techniques to Radar Target Doppler Recognition: A Case Study , 2006 .

[21]  B. P. Bogert,et al.  The quefrency analysis of time series for echoes : cepstrum, pseudo-autocovariance, cross-cepstrum and saphe cracking , 1963 .

[22]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.