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A novel feature extraction method for radar target classification using fusion of early-time and late-time regions

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  • Seung-Jae Lee
  • In-Sik Choi
  • Dae-Young Chae

Abstract

This paper proposes a feature vector fusion of early-time and late-time regions, which improves the performance of radar target classification. For verifying the performance of the proposed method, we use the calculated radar cross section (RCS) of four full-scale targets and measured the RCS of three scale model targets. Then, we extract a feature vector from a waveform structure in the early-time region. The resonance frequencies are extracted using an evolutionary programming (EP)-based CLEAN algorithm in the late-time region. The extracted feature vectors are passed through the feature fusion process and then used as inputs for a neural network classifier. The results show that the proposed method exhibits better performance than those that use either early-time or late-time features.

Suggested Citation

  • Seung-Jae Lee & In-Sik Choi & Dae-Young Chae, 2017. "A novel feature extraction method for radar target classification using fusion of early-time and late-time regions," Journal of Electromagnetic Waves and Applications, Taylor & Francis Journals, vol. 31(10), pages 1020-1033, July.
    • Handle: RePEc:taf:tewaxx:v:31:y:2017:i:10:p:1020-1033
    DOI: 10.1080/09205071.2017.1324324
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