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Application of Self-organizing Feature Neural Network for Target Feature Extraction

  • Dong-hong Liu
  • Zhi-jie Chen
  • Wen-long Hu
  • Yong-shun Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

An effective method, extending Prony algorithm based on Self-organizing feature map neural network, is introduced for radar target feature extraction. The method is a modified classical Prony method based on singular value decomposition and excellent classified capability of Self-organizing feature map neural network which can improved robust for spectrum estimation. Simulation results show that poles and residues of target echo can be extracted effectively using this method, at the same time, random noises are restrained in some degree. It is applicable to target feature extraction such as UWB radar or the other high resolution range radar.

Keywords

Mean Square Error Radial Basis Function Neural Network Target Recognition Radar Target Natural Resonance Frequency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dong-hong Liu
    • 1
  • Zhi-jie Chen
    • 2
  • Wen-long Hu
    • 2
  • Yong-shun Zhang
    • 1
  1. 1.Air Force Engineering UniversitySanyuanChina
  2. 2.Equipment Academy of Air ForceBeijingChina

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