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WU Zezhou, PANG Chen, LI Danyang, et al. Identification method for icosahedron triangular trihedral corner reflector and vessels based on polarization feature-range joint matrix[J]. Journal of Radars, in press. doi: 10.12000/JR25061
Citation: WU Zezhou, PANG Chen, LI Danyang, et al. Identification method for icosahedron triangular trihedral corner reflector and vessels based on polarization feature-range joint matrix[J]. Journal of Radars, in press. doi: 10.12000/JR25061
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Identification Method for Icosahedron Triangular Trihedral Corner Reflector and Vessels Based on Polarization Feature-range Joint Matrix

DOI: 10.12000/JR25061 CSTR: 32380.14.JR25061
  • 1.

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

  • 2.

    School of Physics, Xidian University, Xi’an 710000, China

Funds:  The National Natural Science Foundation of China (62471470, 62301580, 62171447), The science and technology innovation Program of Hunan Province (2024RC3138)
More Information
  • Corresponding author: PANG Chen, pangchen1017@hotmail.com
  • Received Date: 2025-04-01
  • Rev Recd Date: 2025-06-06
    • Available Online: 2025-06-19
    Abstract
  • In the field of radar target recognition, the introduction of Icosahedron Triangular Trihedral Corner Reflector (ITTCR) has increased the difficulty of target identification tasks, especially under moderate to high sea states. Under such conditions, the undulating sea surface can couple with an ITTCR to produce scattering characteristics similar to those of the target, resulting in a decline in the performance of traditional target identification methods. As a solution, a joint matrix of polarization features and range was constructed by considering the dominant scattering mechanisms and scattering complexity. This matrix characterizes the component-level differences between ships and ITTCR arrays in the presence of sea clutter. Subsequently, a temporal neural network extracts features from the joint matrices of the vessels and ITTCR arrays, achieving effective target identification. The performance of the proposed method was validated using a dataset. The proposed method effectively reduces information loss during manual knowledge refinement. Under moderate to high sea states, the proposed method has an accuracy higher than that of the existing methods by 10.14%. Furthermore, the proposed method considerably reduces false alarms caused by ITTCR arrays.

     

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