Volume 10 Issue 1
Feb.  2021
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Article Navigation >  Journal of Radars  > 2021  >  10(1): 61-73
QUAN Sinong, FAN Hui, DAI Dahai, et al. Recognition of ships and chaff clouds based on sophisticated polarimetric target decomposition[J]. Journal of Radars, 2021, 10(1): 61–73. doi: 10.12000/JR20123
Citation: QUAN Sinong, FAN Hui, DAI Dahai, et al. Recognition of ships and chaff clouds based on sophisticated polarimetric target decomposition[J]. Journal of Radars, 2021, 10(1): 61–73. doi: 10.12000/JR20123
shu

Recognition of Ships and Chaff Clouds Based on Sophisticated Polarimetric Target Decomposition

DOI: 10.12000/JR20123
  • 1.

    The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China

  • 2.

    College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China

  • 3.

    College of Electronic Science, National University of Defense Technology, Changsha 410073, China

Funds:  Science Foundation for Youth of The National Natural Science Foundation of China (62001487), Outstanding Youth Fund of Hunan Science Committee (2017JJ1006)
More Information
  • Corresponding author: FAN Hui, fh_luckygirl@163.com
  • Received Date: 2020-09-01
  • Rev Recd Date: 2020-10-22
    • Available Online: 2020-11-16
  • Publish Date: 2021-02-25
    Abstract
  • The recognition of ships from chaff cloud jamming is challenging because they have similar dimensions and radar cross sections. In this paper, we propose a polarimetric recognition technique with sophisticated polarimetric target decomposition. Three sophisticated scattering models are integrated to constitute a seven-component model-based decomposition method so as to accurately characterize the dominant and local scattering of ships. Based on the concepts of contrast and suppression, a robust scattering contribution difference feature is designed according to the derived scattering contributions. The constructed feature vector, combined with the polarization scattering angle, is inputted into the support vector machine to fulfill the recognition process. Simulated and real polarimetric radar data are utilized to test the proposed method, and the results show that the proposed method outperforms state-of-the-art methods by achieving the highest recognition rate of over 98%.

     

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