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Radar Emitter Signal Recognition Based on Resemblance Coefficient Features

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Rough Sets and Current Trends in Computing (RSCTC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3066))

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Abstract

Resemblance coefficient (RC) feature extraction approach for radar emitter signals was proposed. Definition and properties of RC were given. Feature extraction algorithm based on RC was described in detail and the performances of RC features were also analyzed. Neural network classifiers were designed. Theoretical analysis results and simulation experiments of 9 typical radar emitter signal feature extraction and recognition show that RC features are not sensitive to noise and average accurate recognition rate rises to 99.33%, which indicates that the proposed approach is effective.

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Authors and Affiliations

  1. School of Electrical Engineering, Southwest Jiaotong University Chengdu, 610031, Sichuan, China

    Gexiang Zhang & Weidong Jin

  2. School of Computer and Communication Engineering, Southwest Jiaotong University Chengdu, 610031, Sichuan, China

    Haina Rong

  3. National EW Laboratory, Chengdu, 610031, Sichuan, China

    Gexiang Zhang & Laizhao Hu

Authors
  1. Gexiang Zhang
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  2. Haina Rong
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  3. Weidong Jin
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  4. Laizhao Hu
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Editor information

Editors and Affiliations

  1. Shimane University, 89-1 Enya-cho Izumo, 6938501, Shimane, Japan

    Shusaku Tsumoto

  2. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

  3. The Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden

    Jan Komorowski

  4. Institute of Computer Science, Polish Academy of Sciences, 01–237, Warsaw, Poland

    Jerzy W. Grzymała-Busse

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© 2004 Springer-Verlag Berlin Heidelberg

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Zhang, G., Rong, H., Jin, W., Hu, L. (2004). Radar Emitter Signal Recognition Based on Resemblance Coefficient Features. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_83

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  • DOI: https://doi.org/10.1007/978-3-540-25929-9_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22117-3

  • Online ISBN: 978-3-540-25929-9

  • eBook Packages: Springer Book Archive

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