Gabor Filter Approach to Joint Feature Extraction and Target Recognition

This paper presents a new approach of improving automatic target recognition (ATR) performance by tuning adaptively the Gabor filter. The Gabor filter adopts the network structure of two layers, and its input layer constitutes the adaptive nonlinear feature extraction part, whereas the weights between output layer and input layer constitute the linear classifier. From the statistic property of high-resolution range profile (HRRP), its extracted nonstationarity degree of features is tracked to extract the discriminative features of Gabor atoms. Two experimental examples show that the Gabor filter approach with simple structure has higher recognition rate in radar target recognition from HRRP as compared with several existing methods.

[1]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

[2]  Boualem Boashash,et al.  Methods of signal classification using the images produced by the Wigner-Ville distribution , 1991, Pattern Recognit. Lett..

[3]  Friedrich Jondral,et al.  Classification of piano sounds using time-frequency signal analysis , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Robert L. Williams,et al.  Automatic target recognition of time critical moving targets using 1D high range resolution (HRR) radar , 1999 .

[5]  Shie Qian,et al.  Joint time-frequency transform for radar range-Doppler imaging , 1998 .

[6]  Qinghua Zhang,et al.  Wavelet networks , 1992, IEEE Trans. Neural Networks.

[7]  Demetri Psaltis,et al.  Correlation filters for aircraft identification from radar range profiles , 1993 .

[8]  David G. Stork,et al.  Pattern Classification , 1973 .

[9]  A. Zyweck,et al.  Radar target classification of commercial aircraft , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[10]  H. Dickhaus,et al.  Classifying biosignals with wavelet networks [a method for noninvasive diagnosis] , 1996 .

[11]  S. Qian,et al.  Joint time-frequency analysis , 1999, IEEE Signal Process. Mag..

[12]  G. Gaunaurd,et al.  Signal analysis by means of time-frequency (Wigner-type) distributions-applications to sonar and radar echoes , 1996, Proc. IEEE.

[13]  Zhiping Lin,et al.  Local discriminant time-frequency atoms for signal classification , 1999, Signal Process..

[14]  Kemal Leblebicioglu,et al.  ELECTROMAGNETIC TARGET CLASSIFICATION USING TIME : FREQUENCY ANALYSIS AND NEURAL NETWORKS , 1999 .

[15]  Hsueh-Jyh Li,et al.  Using range profiles as feature vectors to identify aerospace objects , 1993 .

[16]  Hao Ling,et al.  Joint time-frequency analysis for radar signal and image processing , 1999, IEEE Signal Process. Mag..

[17]  J. F. Doherty,et al.  A robust echo canceler for acoustic environments , 1997 .

[18]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[19]  Youhong Lu,et al.  Gabor expansion for adaptive echo cancellation , 1999, IEEE Signal Process. Mag..

[20]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[21]  Andrew R. Webb,et al.  Bayesian gamma mixture model approach to radar target recognition , 2003 .

[22]  O. Macchi.  Optimization of adaptive identification for time-varying filters , 1984, The 23rd IEEE Conference on Decision and Control.

[23]  Jun Zhang,et al.  Wavelet neural networks for function learning , 1995, IEEE Trans. Signal Process..

[24]  Joseph A. O'Sullivan,et al.  Automatic target recognition using sequences of high resolution radar range-profiles , 2000, IEEE Trans. Aerosp. Electron. Syst..

[25]  Yu Shi,et al.  A Gabor atom network for signal classification with application in radar target recognition , 2001, IEEE Trans. Signal Process..