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International Conference on Intelligent Science and Big Data Engineering

IScIDE 2018: Intelligence Science and Big Data Engineering pp 243–251Cite as

Radar HRRP Target Recognition with Recurrent Convolutional Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11266))

Abstract

Conventional radar automatic target recognition (RATR) methods using High-Resolution Range Profile (HRRP) sequences require carefully designed feature extraction techniques and plenty of HRRP waveforms, which result in insufficient recognition rate and limit in real-time recognition. To address these issues a modified end-to-end architecture consisting of a convolutional neural network (CNN) followed by a recurrent neural network (RNN) is proposed. In this model the local features of HRRPs extracted by a CNN are passed to a RNN, which avoids manual feature extraction and takes advantage of its shared parameters mechanism which enables single HRRP recognition in real-time. The effectiveness of this model is shown in this paper with numerical results.

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Author information

Authors and Affiliations

  1. National Laboratory of Radar Signal Processing, Xidian University, Xian, China

    Mengqi Shen & Bo Chen

Authors
  1. Mengqi Shen
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  2. Bo Chen
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Correspondence to Mengqi Shen .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Shanghai Jiao Tong University, Shanghai, China

    Kai Yu

  3. Tsinghua University, Beijing, China

    Jiwen Lu

  4. Central China Normal University, Wuhan, China

    Xingpeng Jiang

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Shen, M., Chen, B. (2018). Radar HRRP Target Recognition with Recurrent Convolutional Neural Networks. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_21

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  • DOI: https://doi.org/10.1007/978-3-030-02698-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02697-4

  • Online ISBN: 978-3-030-02698-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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