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Application of Improved Kohonen SOFM Neural Network to Radar Signal Sorting

  • Chuang Zhao
  • Yongjun Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

Abstract

Kohonen neural network is capable of self-organizing and recognizingclustering center, which is used in many artificial intelligence (AI) fields. One electronic support measures (ESM) system must sort the received radar pulses to cells with same features by pulse parameters, such as radio frequency (RF), angle of arrival (AOA), pulse width (PW), Pulse Repetition Interval(PRI), etc. Kohonen SOFM algorithm is one valid method for clustering, which can be used to accomplish such radar pulses sorting. Considering the variety character of pulses parameters which is the character of modern radar system, a new definition of “distance” in the SOFM neural net is proposed in this paper, which decreases the effect of large variety range of special parameter among them. This paper employs the “distance” to improve the clustering capability in such special environments. The computer simulation shows the validity of these improvements.

Keywords

Output Layer Input Pattern Radar System Radar Signal Radar Pulse 
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

  • Chuang Zhao
    • 1
  • Yongjun Zhao
    • 1
  1. 1.ZhengZhou Information Science and Technology InstituteZhengZhouChina

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