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Specific Emitter Identification Based on Feature Selection

  • Yingsen XuEmail author
  • Shilian Wang
  • Luxi Lu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

For the high dimension of fingerprint feature set in the process of specific emitter identification (SEI), feature selection method is utilized to reduce the feature dimension and improve individual recognition rate. This paper adopted the filter feature selection in four ways: MIFS, mRMR, CMIM, and JMIM fingerprint feature set of high-dimensional feature selection and combined with PCA dimensionality reduction algorithm to minimize the feature dimension. The simulation results show that feature selection is feasible in individual recognition of the radiation source and can be effectively combined with dimension reduction algorithm.

Keywords

Specific emitter identification Feature selection Dimension reduction 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Electronic Science and EngineeringNational University of Defense TechnologyChangshaChina
  2. 2.National Key Laboratory of Science and Technology on Blind Signal ProcessingChengduChina

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