A array of the azimuthally averaged range-profile vectors and the inter-class and intra-class divergence matrixes are constructed iwth many frames of the high resolution range profiles which result from radar echoes of airplanes. Taking the methods of whitening transformation and SVD produces a system of subspace vectors for target recognition. Whereupon, a template library for target recognition is built by the projection of a class-mean vector made from the radar data onto the subspace for recognition. By Euclidean distance, a comparison is made between the above projection and each template in the library, to decide which class the target belongs to. Finally, simulations with the experimental radar data arte given to show that the proposed method is robust to variation in azimuth and immune to additive Gaussian noise when SNR≥5dB.
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