한국정보기술학회논문지 (The Journal of Korean Institute of Information Technology)

  • 제17권6호
  • /
  • Pages.85-94
  • /
  • 2019
  • /
  • 1598-8619(pISSN)
  • /
  • 2093-7571(eISSN)
한국정보기술학회 (Korean Institute of Information Technology)
권호 표지
DOI QR코드

DOI QR Code

CFAR와 합성곱 신경망을 이용한 기두부와 단 분리 시 조각 구분

Classification of Warhead and Debris using CFAR and Convolutional Neural Networks

  • 투고 : 2019.04.04
  • 심사 : 2019.05.30
  • 발행 : 2019.06.30
⟨ 이전 논문 다음 논문 ⟩

초록

기두부와 단 분리 시 조각은 서로 다른 미세 운동을 하므로 스펙트로그램 상에서 미세 도플러 주파수의 형태가 서로 다르게 나타나며 이를 통해 구분이 가능하다. 본 논문에서는 합성곱 신경망(CNN : Convolutional Neural Networks)을 이용하여 기두부와 단 분리 시 조각을 구분하였다. 합성곱 신경망의 입력영상으로는 미세도플러 스펙트로그램을 사용하였다. 또한 기두부와 단 분리 시 조각의 구분성능을 향상시키기 위해 미세 도플러 스펙트로그램에 CA-CFAR(Cell Averaging-Constant False Alarm Rate)를 적용하여 전처리 과정을 수행하였다. 실험 결과, 전처리 과정을 수행하여 획득한 미세 도플러 스펙트로그램을 입력 영상으로 사용하였을 경우, 전처리 과정을 수행하지 않은 미세 도플러 스펙트로그램보다 모든 SNR환경에서 구분 성능이 향상되었다.

Warhead and debris show the different micro-Doppler frequency shape in the spectrogram because of the different micro motion. So we can classify them using the micro-Doppler features. In this paper, we classified warhead and debris in the separation phase using CNN(Convolutional Neural Networks). For the input image of CNN, we used micro-Doppler spectrogram. In addition, to improve classification performance of warhead and debris, we applied the preprocessing using CA-CFAR to the micro-Doppler spectrogram. As a result, when the preprocessing of micro-Doppler spectrogram was used, classification performance is improved in all signal-to-noise ratio(SNR).

키워드

참고문헌

  1. H. Gao, L. Xie, S. Wen, and Y. Kuang, "Micro-Doppler signature extraction from ballistic target with micro-motions", IEEE Transaction on Aerospace and Electronic Systems, Vol. 46, No. 4, pp. 1969-1982, Oct. 2010. https://doi.org/10.1109/TAES.2010.5595607
  2. V. C. Chen, F. Li, and S. S. Ho, "Micro-Doppler effect in radar: Phenomenon model and simulation study", IEEE Transaction on Aerospace and Electronic Systems, Vol. 42, No. 1, pp. 2-21, Jan. 2006. https://doi.org/10.1109/TAES.2006.1642557
  3. H. Sun, Z. Liu, and N. Xue, "Ballistic Missile Warhead Recognition based on micro-Doppler Frequency", Defence Science Journal, Vol. 58, No. 6, pp. 705-709, Nov. 2008. https://doi.org/10.14429/dsj.58.1697
  4. A. R. Persico, C. V. Ilioudis, C. Clemente, and J. Soraghan, "Novel Approach for Ballistic Targets Classification from HRRP frame", 2017 Sensor Signal Processing for Defence Conference (SSPD), pp. 1-5, Dec. 2017.
  5. P. Lei, K. Li, and Y. Liu, "Feature extraction and target recognition of missile targets based on micro-motion", 2012 IEEE 11th International Conference on Signal Processing, pp. 1914-1919, Oct. 2012.
  6. Y. J. Choi, I. S. Choi, J. W. Shin, and M. S. Chung, "Classification of the Front Body of a Missile and Debris in Boosting Part Separation Phase Using Periodic and Statistical Properties of Dynamic RCS", Journal of KIEES, Vol. 29, No. 7, pp. 540-549, Jul. 2018.
  7. A. R. Persico, C. Clemente, D. Gaglione, C. V. Ilioudis, J. Cao, L. Pallotta, A. D. Maio, I. Proudler, and J. J. Soraghan, "On Model, Algorithms, and Experiment for micro-Doppler -Based Recognition of Ballistic Targets", IEEE Transaction on Aerospace and Electronic Systems, Vol. 53, No. 3, pp. 1088-1108, Jun. 2017. https://doi.org/10.1109/TAES.2017.2665258
  8. A. Krizhevsky, I. Sutskever, and G. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks", In Proceedings of Advances in Neural Information and Processing Systems 25, pp. 1090-1098, Dec. 2012.
  9. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going Deeper with Convolutions", 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9, Jun. 2015.
  10. K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks For Large-Scale Image Recognition", In Proceedings of International Conference on Learning Representations, pp. 1-14, 2015.
  11. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. F. Fei, "ImageNet large scale visual recognition challenge", International Journal of Computer Vision, Vol. 115, No. 3, pp. 211-252, Dec. 2015. https://doi.org/10.1007/s11263-015-0816-y
  12. R. Wang, J. Lie, Y. Duan, H. Cao, and Y. Zhao, "Study on the Combined Application of CFAR and Deep Learning in Ship Detection", Journal of the Indian Society of Remote Sensing, Vol. 46, No. 9, pp. 1413-1421, Sep. 2018. https://doi.org/10.1007/s12524-018-0787-x
  13. S. S. Seol, I. S. Choi, J. W. Shin, and M. S. Chung, "Design of Convolutional Neural Network Structure for the Identification of Warhead and Debris in the Separation Phase", Journal of KIIT, Vol. 16, No. 6, pp. 81-89, Jun. 2018. https://doi.org/10.14801/jkiit.2018.16.6.81
  14. M. I. Skolnik, Introduction to RADAR systems: 3rd edition, McGraw-Hill, 2001.
  15. https://www.mathworks.com/help/phased/examples/constant-false-alarm-rate-cfar-detection.html. [accessed: Nov. 05, 2018]
  16. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning", Nature, Vol. 521, No. 7553, pp. 436-444, May 2015. https://doi.org/10.1038/nature14539
  17. I. Y. Kang, "Stability and Accuracy Analysis for the Shape of Supersonic Spin-stabilized Projectile", Pusan National University Master's Thesis, 2003.
  18. D. C. Kwon, B. Y. Kang, "CPU and GPU Performance Analysis for Convolution Neural Network", Journal of KIIT, Vol. 15, No. 8, pp. 11-18, Aug. 2017.

피인용 문헌

  1. Separation of Dynamic RCS using Hough Transform in Multi-target Environment vol.17, pp.9, 2019, https://doi.org/10.14801/jkiit.2019.17.9.91
  2. Dynamic RCS Calculation of Wind Turbine Blade Using GPU-Based TSM-RT vol.31, pp.3, 2019, https://doi.org/10.5515/kjkiees.2020.31.3.245