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A Study on the Performance Enhancement of Radar Target Classification Using the Two-Level Feature Vector Fusion Method

  • Kim, In-Ha (Department of Electronic Engineering, Hannam University) ;
  • Choi, In-Sik (Department of Electronic Engineering, Hannam University) ;
  • Chae, Dae-Young (Agency for Defense Development)
  • Received : 2017.12.11
  • Accepted : 2018.06.19
  • Published : 2018.07.31

Abstract

In this paper, we proposed a two-level feature vector fusion technique to improve the performance of target classification. The proposed method combines feature vectors of the early-time region and late-time region in the first-level fusion. In the second-level fusion, we combine the monostatic and bistatic features obtained in the first level. The radar cross section (RCS) of the 3D full-scale model is obtained using the electromagnetic analysis tool FEKO, and then, the feature vector of the target is extracted from it. The feature vector based on the waveform structure is used as the feature vector of the early-time region, while the resonance frequency extracted using the evolutionary programming-based CLEAN algorithm is used as the feature vector of the late-time region. The study results show that the two-level fusion method is better than the one-level fusion method.

Keywords

Bistatic Radar;Feature Vector;Feature Vector Fusion;Monostatic Radar;Target Classification

Acknowledgement

Supported by : National Research Foundation of Korea

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