Feature Extraction Method of Narrow-band Radar Airplane Signatures Based on Fractional Fourier Transform

DU Lan; SHI Huiruo; LI Linsen; SUN Yongguang; HU Jing

doi:10.11999/JEIT161035

Volume 38 Issue 12

Jan. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(12): 3093-3099

DU Lan, SHI Huiruo, LI Linsen, SUN Yongguang, HU Jing. Feature Extraction Method of Narrow-band Radar Airplane Signatures Based on Fractional Fourier Transform[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3093-3099. doi: 10.11999/JEIT161035

Citation:

DU Lan, SHI Huiruo, LI Linsen, SUN Yongguang, HU Jing. Feature Extraction Method of Narrow-band Radar Airplane Signatures Based on Fractional Fourier Transform[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3093-3099. doi: 10.11999/JEIT161035

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Feature Extraction Method of Narrow-band Radar Airplane Signatures Based on Fractional Fourier Transform

doi: 10.11999/JEIT161035

Funds:

The National Natural Science Foundation of China (61271024, 61322103), The Foundation for Doctoral Supervisor of China (20130203110013), The Natural Science Foundation of Shaanxi Province (2015JZ016)

Received Date: 2016-10-08
Rev Recd Date: 2016-12-01
Publish Date: 2016-12-19

Abstract

Abstract

This paper studies on the feature extraction methods for the classification of helicopter, propeller-driven aircraft, and turbojet using a conventional narrow-band radar system. In the modern battlefield, the helicopter, propeller aircraft and jet aircraft with different motor performances each bear an important task. But the classification performance of the traditional features for the three types of aircraft target classification is not good enough, so the Fractional Fourier Transform (FrFT) is introduced. Based on the existing feature extraction method, the fractional order features of three kinds of aircraft targets are extracted from the fractional domain after FrFT to extend feature domain. Then, the effective features are selected from all extracted features and the classification of the three categories via linear Relevance Vector Machine (RVM) is realized. The experiments demonstrate that the proposed fractional features can improve the classification performance in comparison with some existing features from the time-domain and Doppler-frequency domain.
- Narrow band radar,
- Fractional Fourier Transform (FrFT),
- Feature extraction,
- Feature selection,
- Target classification,
- Jet Engine Modulation (JEM)

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References(24)

References

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