Volume 14 Issue 2
Apr.  2025
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Article Navigation >  Journal of Radars  > 2025  >  14(2): 293-308
WANG Bohong, SHEN Biao, MU Wenxing, et al. Research on super-resolution methods for radar targets based on bat-inspired spectrogram correlation and transformation models[J]. Journal of Radars, 2025, 14(2): 293–308. doi: 10.12000/JR24239
Citation: WANG Bohong, SHEN Biao, MU Wenxing, et al. Research on super-resolution methods for radar targets based on bat-inspired spectrogram correlation and transformation models[J]. Journal of Radars, 2025, 14(2): 293–308. doi: 10.12000/JR24239
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Research on Super-resolution Methods for Radar Targets Based on Bat-inspired Spectrogram Correlation and Transformation Models

DOI: 10.12000/JR24239 CSTR: 32380.14.JR24239

  • School of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China

Funds:  The National Natural Science Foundation of China (62171452)
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  • Corresponding author: LIU Tao, liutao1018@sina.com
  • Received Date: 2024-12-01
  • Rev Recd Date: 2025-02-19
    • Available Online: 2025-02-22
  • Publish Date: 2025-03-17
    Abstract
  • The resolving power of traditional radar is mainly analyzed using the ambiguity function, and its limit is generally characterized by the Rayleigh limit. Bats have a rather sensitive auditory system. Researchers have proposed the Spectrogram Correlation And Transformation (SCAT) model to represent the auditory system of bats, explored their super-resolution principle, and provided a possible means to break through the conventional (Rayleigh) resolving power limit of radar targets. To further enhance the discriminative performance of the SCAT model, two bat-auditory-system-based super-resolution models, namely the base vector deconvolution method and Baseband SCAT (BSCT), are improved by suppressing redundant wave flaps at the negative semiaxis of the range profile and at the origin. Meanwhile, the concept and computation method of reliable discriminative power are proposed to unify the measurements of SCAT and Rayleigh discriminative powers. Further, a comparison is made to validate the rationality of the concept of reliable discriminative power, and the effectiveness of the improved models is verified. Simulation and real experiments show that the improved super-resolution models achieve a sizable increase in the resolving power. Notably, the improved base vector deconvolution method performs the best, improving the resolving power of the original method by ～2 dB while enhancing the matched filtering resolving power by ～5 dB.

     

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