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A Novel Sorting Method Based on Auto-correlation Function of Coherent Radar Signal

Received: 5 April 2017     Published: 6 April 2017
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Abstract

In order to solve the increasing-batch problem in signal sorting, a novel sorting method based on auto-correlation function of coherent radar signal is proposed. By using the instantaneous auto-correlation function of coherent signal, the discrimination model of ratio of peak values was built and applied in sorting simulation. Simulation results show that the proposed approach can solve the Increasing-batch problem for CW, LFM, NLFM one sorting when the signal-to-noise rate (SNR) above 5dB. The validity of the approach is demonstrated by experimental results.

Published in Journal of Electrical and Electronic Engineering (Volume 5, Issue 2)
DOI 10.11648/j.jeee.20170502.14
Page(s) 48-52
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Sorting, Coherency, Auto-correlation Function, Ratio of Peak Values

References
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[2] Nishguchi K, Kobayashi M. Improved algorithm for estimating pulse repetition intervals [J]. IEEE Trans on AES, 2000, 36(2): 407-421.
[3] Pu Yunwei, Jin Weidong, Zhu Ming, Hu Laizhao. Extracting the main ridge slice characteristics of ambiguity function for radar emitter signals [J]. Journal of Infrared and Millimeter Waves, 2008, 27(2): 133-137.
[4] Zhu Ming, Jin Weidong, Hu Laizhao. Cascade feature Extraction for Radar Emitter Signals Based on Atomic Decomposition [J]. Journal of Southwest Jiaotong University, 2007, 42(6): 659-664.
[5] Hu Hongying, Kang Jing, and Guan Lina. Instantaneous frequency estimation based on empirical mode decomposition [C]. Proceedings of the 7th Wor1d Congress on Intelligent Control and Automation, Chongqing, China, June 25-27, 2008.
[6] Huang Zhitao, Zhou Yiyu, Jiang Wenli. The automatic analysis of intrapulse modulation characte- ristics based on the relatively non-ambiguity phase restoral [J]. Journal on Communications, 2003, 24(4): 153-160.
[7] Han Jun, He Minghao, Zhu Yuanqing, Mao Yan. Sorting of Radar Emitter Based on Multi-Parameters [J]. Journal of Data Acquisition & Processing, 2009, 24(1): 91-94.
[8] Meng Xianghao, Luo Jingqing. Radar Signal Sorting Based on Compositive Characteristic Parameter and Pulse Pattern [J]. Fire Control & Command Control, 2012, 37(9): 50-54.
[9] Li Yaojun, Li Ping, Chu Fulei. Multi-vibration signal data fusion method based on correlation function [J]. Journal of Vibration, Measurement & Diagnosis, 2009, 29(2): 179-183.
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Cite This Article
  • APA Style

    Wei Qing-yun, Chen Bei, Wang Jing, Han Jun, Tang Yu-wen. (2017). A Novel Sorting Method Based on Auto-correlation Function of Coherent Radar Signal. Journal of Electrical and Electronic Engineering, 5(2), 48-52. https://doi.org/10.11648/j.jeee.20170502.14

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    ACS Style

    Wei Qing-yun; Chen Bei; Wang Jing; Han Jun; Tang Yu-wen. A Novel Sorting Method Based on Auto-correlation Function of Coherent Radar Signal. J. Electr. Electron. Eng. 2017, 5(2), 48-52. doi: 10.11648/j.jeee.20170502.14

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    AMA Style

    Wei Qing-yun, Chen Bei, Wang Jing, Han Jun, Tang Yu-wen. A Novel Sorting Method Based on Auto-correlation Function of Coherent Radar Signal. J Electr Electron Eng. 2017;5(2):48-52. doi: 10.11648/j.jeee.20170502.14

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  • @article{10.11648/j.jeee.20170502.14,
      author = {Wei Qing-yun and Chen Bei and Wang Jing and Han Jun and Tang Yu-wen},
      title = {A Novel Sorting Method Based on Auto-correlation Function of Coherent Radar Signal},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {5},
      number = {2},
      pages = {48-52},
      doi = {10.11648/j.jeee.20170502.14},
      url = {https://doi.org/10.11648/j.jeee.20170502.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20170502.14},
      abstract = {In order to solve the increasing-batch problem in signal sorting, a novel sorting method based on auto-correlation function of coherent radar signal is proposed. By using the instantaneous auto-correlation function of coherent signal, the discrimination model of ratio of peak values was built and applied in sorting simulation. Simulation results show that the proposed approach can solve the Increasing-batch problem for CW, LFM, NLFM one sorting when the signal-to-noise rate (SNR) above 5dB. The validity of the approach is demonstrated by experimental results.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - A Novel Sorting Method Based on Auto-correlation Function of Coherent Radar Signal
    AU  - Wei Qing-yun
    AU  - Chen Bei
    AU  - Wang Jing
    AU  - Han Jun
    AU  - Tang Yu-wen
    Y1  - 2017/04/06
    PY  - 2017
    N1  - https://doi.org/10.11648/j.jeee.20170502.14
    DO  - 10.11648/j.jeee.20170502.14
    T2  - Journal of Electrical and Electronic Engineering
    JF  - Journal of Electrical and Electronic Engineering
    JO  - Journal of Electrical and Electronic Engineering
    SP  - 48
    EP  - 52
    PB  - Science Publishing Group
    SN  - 2329-1605
    UR  - https://doi.org/10.11648/j.jeee.20170502.14
    AB  - In order to solve the increasing-batch problem in signal sorting, a novel sorting method based on auto-correlation function of coherent radar signal is proposed. By using the instantaneous auto-correlation function of coherent signal, the discrimination model of ratio of peak values was built and applied in sorting simulation. Simulation results show that the proposed approach can solve the Increasing-batch problem for CW, LFM, NLFM one sorting when the signal-to-noise rate (SNR) above 5dB. The validity of the approach is demonstrated by experimental results.
    VL  - 5
    IS  - 2
    ER  - 

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Author Information
  • Air Force Early Warning Academy, Wuhan, China

  • Air Force Early Warning Academy, Wuhan, China

  • Spic Central Research Institute, Beijing, China

  • Air Force Early Warning Academy, Wuhan, China

  • Air Force Early Warning Academy, Wuhan, China

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