Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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Modeling and Analysis of Radar Target Signatures in the VHF-Band Using Fast Chirplet Decomposition

고속 Chirplet 분리기법을 이용한 VHF 대역 레이더 표적신호 모델링 및 해석

  • Park, Ji-hoon (The 3rd Research and Development Institute, Agency of Defense Development) ;
  • Kim, Si-ho (The 3rd Research and Development Institute, Agency of Defense Development) ;
  • Chae, Dae-Young (The 3rd Research and Development Institute, Agency of Defense Development)
  • 박지훈 (국방과학연구소 제3기술연구본부) ;
  • 김시호 (국방과학연구소 제3기술연구본부) ;
  • 채대영 (국방과학연구소 제3기술연구본부)
  • Received : 2019.04.04
  • Accepted : 2019.06.07
  • Published : 2019.08.05
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Abstract

Although radar target signatures(RTS), such as range profiles have played an important role for target recognition in the X-band radar, they would be less effective when a target is designed to have low radar cross section(RCS). Recently, a number of research groups have conducted the studies on the RTS in the VHF-band where such targets can be better detected than in the X-band. However, there is a lack of work carried out on the mathematical description of the VHF-band RTS. In this paper, chirplet decomposition is employed for modeling of the VHF-band RTS and its performance is compared with that of existing scattering center model generally used for the X-band. In addition, the discriminative signal analysis is performed by chirplet parameterization of range profiles from in an ISAR image. Because the chirplet decomposition takes long computation time, its fast form is further proposed for enhanced practicality.

Keywords

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Fig. 1. Overall RTS modeling process

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Fig. 2. Chirplet 1D/2D waveform with different durations and chirp rates

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Fig. 3. ISAR image consisting of range profiles

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Fig. 4. Range profile modeling performance of different modeling methods

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Fig. 5. Modeling of range profiles using proposed chirplet model and scattering center model

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Fig. 6. ISAR image reconstructed by chirplets

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Fig. 7. ISAR images selectively reconstructed by durations ((a) : short chirplets, (b) : long chirplets)

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Fig. 8. ISAR images reconstructed by chirp rate((a) : short/fast chirplets, (b) : short/slow chirplets)

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Fig. 9. ISAR images reconstructed by chirp rate((a) : long/fast chirplets, (b) : long/slow chirplets)

Table 1. Mean correlation coefficient and mean computation time of each modeling method

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References

  1. P. Tait, "Introduction to Radar Target Recognition," The Institution of Engineering and Technology, London United Kingdom, pp. 105-181, 2005.
  2. K. Zikidis, A. Skondras and C. Tokas, “Low Observable Principles, Stealth Aircraft and Anti- Stealth Technologies,” Journal of Computations and Modelling, Vol. 4, No. 1, pp. 129-165, 2014.
  3. L. M. H. Ulander, et. al., "Development of the Ultra-Wideband LORA SAR Operating in the VHF/UHF-Band," IEEE International Geoscience and Remote Sensing Symposium, 2003.
  4. L. M. H. Ulander, et. al., "Bistatic Experiment with Ultra-Wideband VHF-Band Synthetic Aperture Radar," 7th European Conference on Synthetic Aperture Radar, 2008.
  5. J. Li and H. Ling, “Application of Adaptive Chirplet Representation for ISAR Feature Extraction from Targets with Rotating Parts,” IEE Proc. Radar Sonar Navig., Vol. 150, No. 4, pp. 284-291, 2003. https://doi.org/10.1049/ip-rsn:20030729
  6. F. Millioz and M. Davies, “Sparse Detection in the Chirplet Transform: Application to FMCW Radar Signals,” IEEE Trans. Signal. Process., Vol. 60, No. 6, pp. 2800-2813, 2012. https://doi.org/10.1109/TSP.2012.2190730
  7. O. Yeste-Ojeda, J. Grajal and G. Lopez-Risueno, “Atomic Decomposition for Radar Applications,” IEEE Trans. Aerosp. Electron. Syst., Vol. 44, No. 1, pp. 187-200, 2008. https://doi.org/10.1109/TAES.2008.4516998
  8. A. Bultan, “A Four-Parameter Atomic Decomposition of Chirplets,” IEEE Trans. Signal. Process., Vol. 47, No. 3, pp. 731-745, 1999. https://doi.org/10.1109/78.747779
  9. J. C. O'Neill, P. Flandrin and W. C. Carl, "Sparse Representation with Chirplets via Maximum Likelihood Estimation," 2000. Paper available at http://tfd.source forge.net/Papers/chirp.pdf.
  10. Q. Yin, S. Qian and F. Aigang, “A Fast Refinement for Adaptive Gaussian Chirplet Decomposition,” IEEE Trans. Signal Process., Vol. 50, No. 6, pp. 1298-1306, 2002. https://doi.org/10.1109/TSP.2002.1003055