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Radar Pattern Classification Based on Class Probability Output Networks

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9489))

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Abstract

Modern aircraft and ships are equipped with radars emitting specific patterns of electromagnetic signals. The radar antennas are detecting these patterns which are required to identify the types of emitters. A conventional way of emitter identification is to categorize the radar patterns according to the sequences of frequencies, time of arrivals, and pulse widths of emitting signals by human experts. In this respect, this paper presents a method of classifying the radar patterns automatically using the network of calculating the p-values of testing the hypotheses of the types of emitters referred to as the class probability output network (CPON). Through the simulation for radar pattern classification, the effectiveness of the proposed approach has been demonstrated.

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Author information

Authors and Affiliations

  1. College of Information and Communication Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 440-746, Korea

    Lee Suk Kim & Rhee Man Kil

  2. Electronic Warfare R&D Laboratory, LIGNex1 Co., Ltd., 333, Pangyo-ro, Bundang-gu, Seongnam City, Gyeonggi-do, 463-400, Korea

    Churl Hee Jo

Authors
  1. Lee Suk Kim
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  2. Rhee Man Kil
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  3. Churl Hee Jo
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Corresponding author

Correspondence to Rhee Man Kil .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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© 2015 Springer International Publishing Switzerland

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Kim, L.S., Kil, R.M., Jo, C.H. (2015). Radar Pattern Classification Based on Class Probability Output Networks. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_53

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  • DOI: https://doi.org/10.1007/978-3-319-26532-2_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26531-5

  • Online ISBN: 978-3-319-26532-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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