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Target Detection in Sea Clutter Based on ELM

  • Wei Jing
  • Guangrong Ji
  • Shiyong Liu
  • Xi Wang
  • Ying Tian
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 812)

Abstract

Target detection is a hot topic in the research of sea clutter. The solution of this problem can be divided into two aspects. Firstly, find out the different characteristics between the target and sea clutter. Secondly, take advantage of the classifier to realize the feature classification. Thus, we study the characteristics of sea clutter. As a result, the decorrelation time, the K distribution fitting parameters and the Hurst exponent in the FRFT domain are proved to be three feature vectors that can better distinguish the target from sea clutter. Finally, we bring the Extreme Learning Machine (ELM) in the feature classification. Experiment results demonstrate that the chosen feature vectors are effective. Moreover, the ELM is also effective by comparison with SVM.

Keywords

Target detection Sea clutter Feature vectors Extreme Learning Machine 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Wei Jing
    • 1
  • Guangrong Ji
    • 1
  • Shiyong Liu
    • 1
  • Xi Wang
    • 1
  • Ying Tian
    • 1
  1. 1.Department of Computer Science and TechnologyOcean University of ChinaQingdaoChina

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