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신호 모델링 기법을 이용한 소총화기 신호 검출에 대한 연구

A Study on the Detection of Small Arm Rifle Sound Using the Signal Modelling Method

  • 신민철 (단국대학교 소프트웨어학과) ;
  • 박규식 (단국대학교 소프트웨어학과)
  • 투고 : 2015.04.08
  • 심사 : 2015.04.29
  • 발행 : 2015.07.15

초록

본 논문에서는 신호 모델링 기법을 이용하여 소총화기에서 발생하는 탄환충격파(SW, Shock Wave) 음향신호와 총성(MB, Muzzle Blast) 음향신호를 효과적으로 검출할 수 있는 알고리즘을 제안하였다. 전장에서 저격수의 위치를 탐지하기 위해서는 저격수의 소총화기에서 발생하는 탄환충격파와 총성 신호를 정확하게 검출하여 적 저격수의 방향각과 거리를 추정하는 것이 중요하다. 제안 알고리즘의 성능을 검증하기 위하여 국내 군 사격장에서 실제 소총화기 발사 실험을 진행하였고, 실험결과 제안 알고리즘은 탄환충격파 신호 검출에 있어 기존 알고리즘에 비해 최대 20% 가까운 성능향상을, 총성 신호 검출에 있어서는 약 5% 정도의 성능향상을 가져옴을 확인할 수 있었다.

This paper proposes a signal modelling method that can effectively detect the shock wave(SW) sound and muzzle blast(MB) sound from the gunshot of a small arm rifle. In order to localize a counter sniper in battlefield, an accurate detection of both shock wave sound and muzzle blast sound are the necessary keys in estimating the direction and the distance of the counter sniper. To verify the performance of the proposed algorithm, a real gunshot sound in a domestic military shooting range was recorded and analyzed. From the experimental results, the proposed signal modelling method was found to be superior to the comparative system more than 20% in a shock wave detection and 5% in a muzzle blast detection, respectively.

키워드

과제정보

연구 과제 주관 기관 : 방위사업청

참고문헌

  1. D. FILKINS and R. F. WORTH (2004, Nov. 10). [Online] "U.S -Led Assault MarksAdvances Against Falluja,"Available:http://www.nytimes.com/2004/11/10/ international /middleeast/10falluja.html?8bl.
  2. S. Yoon, "Sniper Plays a Key Role in Small Scale Battlefield," Korea Army, No. 78, pp. 57-61, Nov. 2005.
  3. C. Choi and S. Lee, "A Trend on acoustic target equipment," National Defense and Technology, No. 386, pp. 46-59, Apr. 2011.
  4. G.L. Duckworth, D.C. Gilbert, J.E. Barger, "Acoustic Counter-Sniper System," Proc. of SPIE International Symposium on Enabling Technologies for Law Enforcement and Security, Vol. 2938, pp. 262-275, Nov. 1996.
  5. E. Daniki, "The shock wave-based acoustic sniper localization," Nonlinear analysis: theory, methods & applications, Elsevier, Vol. 65, pp. 956-962, Sep. 2006. https://doi.org/10.1016/j.na.2005.07.043
  6. T. Makinen and P. Pertila, "Shooter Localization and Bullet trajectory, Caliber, and Speed Estimation Based on Detected Firing Sounds," Applied Acoustics, Elsevier, Vol. 71, pp. 902-913, Jun. 2010. https://doi.org/10.1016/j.apacoust.2010.05.021
  7. Metravib (2004), [Online]. Available: http://me travib.acoemgroup.com/defencecatalog/METRAVIB-PILARw.
  8. G. Duckworth, J. E. Barger, D. C. Gilbert, Acoustic Counter-sniper System, U.S. Patent 6,178,141, Jan. 2001.
  9. Raytheon BBN Technologies (2015). [Online]. "Boomerang3," Available:http://www.raytheon.com/capabilities/products /boomerang/
  10. QinetiQ. (2008, Mar. 12). [Online] "EARS - QinetiQ's Battle-Proven Sniper Detection Solution," Available: http://www.qinetiq.com/media/news/releases/Pages/ears.aspx
  11. Raytheon BBN Technologies. (2011, Oct. 10). [Online]. "US Army orders $9 million in additional Boomerang components," Available: http://investor.raytheon.com/phoenix.zhtml?c=84193&p=irol-newsArticle&ID=1615231
  12. G. Simon, M. Maroti, A. Ledeczi, et. al., "Sensor network-based countersniper system," Proc. of the 2nd international conference on Embedded networked sensor systems, ACM Press, pp. 1-12, 2004.
  13. D. Crane. (2006, July 19). [Online]. "Anti-Sniper/Sniper Detection/Gunfire Detection Systems at a Glance", Available: http://www.defensereview.com/antisnipersniper-detectiongunfire-detection-systems-at-a-glance/
  14. R. C. Maher, "Acoustical characterization of gunshots," Proc. of IEEE SAFE 2007: Workshop on Signal Processing Applications for Public Security and Forensics, pp. 109-113, 2007.
  15. R. C. Maher and S. R. Shaw, "Deciphering gunshot recordings," Proc. of Audio Engineering Society 33rd Conf., Audio Forensics: Theory and Practice, pp. 1-8, Jun. 2008.