Radar target recognition using a radial basis function neural network

The problem of radar target recognition based on high resolution range profiles is investigated in this paper, using a radial basis function network (RBFN). After analysing the signatures of the range profile, an effective preprocessing method is proposed to obtain stable and shift invariant patterns which are classified by RBFN. Then the classification mechanism of RBFN is described, suggesting that the RBFN has better performance than the conventional kernel classifier. It is shown from theoretical analysis and experimental results which were obtained with data acquired in a microwave anechoic chamber that the method proposed in this paper offers promise for target recognition.

[1]  H. Bourlard,et al.  Links Between Markov Models and Multilayer Perceptrons , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Halbert White,et al.  Learning in Artificial Neural Networks: A Statistical Perspective , 1989, Neural Computation.

[3]  Licheng Jiao,et al.  Target recognition based on radial basis function network , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[4]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[5]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.

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

[7]  Bernard Widrow,et al.  Adaptive Signal Processing , 1985 .

[8]  Eric A. Wan,et al.  Neural network classification: a Bayesian interpretation , 1990, IEEE Trans. Neural Networks.

[9]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[10]  Patrick A. Shoemaker,et al.  A note on least-squares learning procedures and classification by neural network models , 1991, IEEE Trans. Neural Networks.

[11]  Bruce W. Suter,et al.  The multilayer perceptron as an approximation to a Bayes optimal discriminant function , 1990, IEEE Trans. Neural Networks.

[12]  M. W. Roth.  Survey of neural network technology for automatic target recognition , 1990, IEEE Trans. Neural Networks.

[13]  D. Mensa.  High resolution radar imaging , 1981 .

[14]  Sukhan Lee,et al.  A Gaussian potential function network with hierarchically self-organizing learning , 1991, Neural Networks.

[15]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[16]  H. Gish,et al.  A probabilistic approach to the understanding and training of neural network classifiers , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[17]  James D. Keeler,et al.  Layered Neural Networks with Gaussian Hidden Units as Universal Approximations , 1990, Neural Computation.

[18]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[19]  L. Devroye,et al.  Nonparametric Density Estimation: The L 1 View. , 1985 .

[20]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.