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An Efficient Kernel Optimization Method for Radar High-Resolution Range Profile Recognition

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Abstract

A kernel optimization method based on fusion kernel for high-resolution range profile (HRRP) is proposed in this paper. Based on the fusion of-norm and-norm Gaussian kernels, our method combines the different characteristics of them so that not only is the kernel function optimized but also the speckle fluctuations of HRRP are restrained. Then the proposed method is employed to optimize the kernel of kernel principle component analysis (KPCA) and the classification performance of extracted features is evaluated via support vector machines (SVMs) classifier. Finally, experimental results on the benchmark and radar-measured data sets are compared and analyzed to demonstrate the efficiency of our method.

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Correspondence to Bo Chen.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Chen, B., Liu, H. & Bao, Z. An Efficient Kernel Optimization Method for Radar High-Resolution Range Profile Recognition. EURASIP J. Adv. Signal Process. 2007, 049597 (2007) doi:10.1155/2007/49597

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Keywords

  • Radar
  • Support Vector Machine
  • Information Technology
  • Support Vector
  • Kernel Function