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YU Xue-lian, LIU Ben-yong. Optimal Kernel Discriminant Analysis for Radar Target Recognition[J]. Journal of University of Electronic Science and Technology of China, 2008, 37(6): 883-885,937.
Citation: YU Xue-lian, LIU Ben-yong. Optimal Kernel Discriminant Analysis for Radar Target Recognition[J]. Journal of University of Electronic Science and Technology of China, 2008, 37(6): 883-885,937.
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Optimal Kernel Discriminant Analysis for Radar Target Recognition

  • 1.

    School of Electronic Engineering,University of Electronic Science and Technology of China Chengdu 610054;

  • 2.

    School of Computer Science and Technology,Guizhou University Guiyang 520025

  • Received Date: 2007-05-29
  • Rev Recd Date: 2008-04-03
  • Publish Date: 2008-12-15
  • Kernel discriminant analysis (KDA) is an effective method for nonlinear feature extraction in radar target recognition, but it is usually difficult to solve due to the singular problem. Based on the idea of subspace projection, an optimal kernel discriminant analysis (OKDA) is given and used to extract features from a range profile. Then, kernel-based nonlinear classifiers are applied for classification. Experimental results on both simulated and measured profiles show comparatively good recognition performance of the proposed method.
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  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

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Optimal Kernel Discriminant Analysis for Radar Target Recognition

  • 1. School of Electronic Engineering,University of Electronic Science and Technology of China Chengdu 610054;
  • 2. School of Computer Science and Technology,Guizhou University Guiyang 520025
  • Received Date: 2007-05-29
  • Rev Recd Date: 2008-04-03
  • Publish Date: 2008-12-15

Abstract: Kernel discriminant analysis (KDA) is an effective method for nonlinear feature extraction in radar target recognition, but it is usually difficult to solve due to the singular problem. Based on the idea of subspace projection, an optimal kernel discriminant analysis (OKDA) is given and used to extract features from a range profile. Then, kernel-based nonlinear classifiers are applied for classification. Experimental results on both simulated and measured profiles show comparatively good recognition performance of the proposed method.

YU Xue-lian, LIU Ben-yong. Optimal Kernel Discriminant Analysis for Radar Target Recognition[J]. Journal of University of Electronic Science and Technology of China, 2008, 37(6): 883-885,937.
Citation: YU Xue-lian, LIU Ben-yong. Optimal Kernel Discriminant Analysis for Radar Target Recognition[J]. Journal of University of Electronic Science and Technology of China, 2008, 37(6): 883-885,937.
shu

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