Volume 10 Issue 4
Aug.  2021
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Article Navigation >  Journal of Radars  > 2021  >  10(4): 559-570
WANG Junjie, FENG Dejun, WANG Zhisong, et al. Synthetic aperture rader imaging characteristics of electronically controlled time-varying electromagnetic materials[J]. Journal of Radars, 2021, 10(6): 865–873. doi: 10.12000/JR21104
Citation: LIU Zhangmeng, YUAN Shuo, and KANG Shiqian. Semantic coding and model reconstruction of multifunctional radar pulse train[J]. Journal of Radars, 2021, 10(4): 559–570. doi: 10.12000/JR21031
shu

Semantic Coding and Model Reconstruction of Multifunction Radar Pulse Train

DOI: 10.12000/JR21031

  • School of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds:  Provincial Outstanding Youth project of Hunan (2020JJ2037), Huxiang Young Talents project of Hunan (2019RS2026), Provincial Innovation Research Group of Hunan (2019JJ10004)
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  • Corresponding author: LIU Zhangmeng, liuzhangmeng@nudt.edu.cn
  • Received Date: 2021-03-15
  • Rev Recd Date: 2021-07-23
    • Available Online: 2021-08-03
  • Publish Date: 2021-08-28
    Abstract
  • Retrieving the working modes of multifunction radar from electronic reconnaissance data is a difficult problem, and it has attracted widespread attention in the field of electronic reconnaissance. It is also an important task when extracting benefits from big electromagnetic data and provides straightforward support to applications, such as radar type recognition, working state recognition, radar intention inferring, and precise electronic jamming. Based on the assumption of model simplicity, this study defines a complexity measurement rule for multifunction radar pulse trains and introduces the semantic coding theory to analyze the temporal structure of multifunction radar pulse trains. The model complexity minimization criterion guides the semantic coding procedure to extract radar pulse groups corresponding to different radar functions from pulse trains. Furthermore, based on the coded sequence of the pulse train, the switching matrix between different pulse groups is estimated, and the hierarchical working model of multifunction radars is ultimately reconstructed. Simulations are conducted to verify the feasibility and performance of the new method. Simulation results indicate that the coding theory is successfully used in the proposed method to automatically extract pulse groups and rebuild operating models based on multifunction radar pulse trains. Moreover, the method is robust to data noises, such as missing pulses.

     

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