한국음향학회지 (The Journal of the Acoustical Society of Korea)

  • 제38권4호
  • /
  • Pages.387-395
  • /
  • 2019
  • /
  • 1225-4428(pISSN)
  • /
  • 2287-3775(eISSN)
한국음향학회 (The Acoustical Society of Korea)
권호 표지
DOI QR코드

DOI QR Code

한국 해양환경에서 음파전달모델을 이용한 표적기동분석 알고리즘

Target motion analysis algorithm using an acoustic propagation model in the ocean environment of South Korea

  • 투고 : 2019.02.27
  • 심사 : 2019.06.17
  • 발행 : 2019.07.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

수동소나에서의 표적기동분석은 방위 또는 방위-주파수와 같이 제한된 정보를 이용하여 수행된다. 표적기동분석을 빠르고 정확하게 수행하기 위해서는 정확한 표적기동 초기치의 추정이 필수적이다. 기존의 표적기동분석과 달리 신호 대 잡음비 정보와 음파전달모델을 추가로 이용하면 표적기동분석의 정확도를 향상시킬 수 있다. 이 방법은 표적의 방사소음수준은 알고 있다고 가정하지만 가정한 수준과 실제 수준간의 오차에 따라 표적기동분석의 정확도가 저하될 수 있다. 본 논문에서는 수동 소나로 탐지한 표적 방위정보, 탐지 신호 대 잡음비 정보 및 음파전달모델을 이용한 표적기동분석 알고리즘을 한국 해양환경(동해/서해/남해)에서 수행한다. 그리고 가정한 표적 방사소음수준과 실제 수준간의 차이에 따른 성능분석 결과를 제시한다.

TMA (Target Motion Analysis) in passive sonar is generally conducted with the bearing only or the bearing frequency. In order to conduct TMA fast and accurately, it is essential to estimate a initial target maneuver precisely. The accuracy of TMA can be improved by using SNR (Signal to Noise Ratio) information and acoustic propagation model additionally. This method assumes that the radiated noise level of the target is known, but the accuracy of TMA can be degraded due to a mismatch between the assumed radiated noise level and the actual radiated noise level. In this paper, TMA with the acoustic propagation model, bearing measurements, and SNR information is conducted in the ocean environment of South Korea (East Sea/ Yellow Sea/ South Sea). And the performance analysis of TMA for the mismatch in the radiated noise is presented.

키워드

GOHHBH_2019_v38n4_387_f0001.png 이미지

Fig. 1. Geometric Relationship between target and ownship.

GOHHBH_2019_v38n4_387_f0002.png 이미지

Fig. 2. Bathythermograph for winter and summer season (a) in East, (b) in Yellow, (c) South Sea.

GOHHBH_2019_v38n4_387_f0003.png 이미지

Fig. 3. Transmission loss.

GOHHBH_2019_v38n4_387_f0004.png 이미지

Fig. 4. Ownship and target maneuvering scenario.

GOHHBH_2019_v38n4_387_f0005.png 이미지

Fig. 5. Graph of range vs SNR.

GOHHBH_2019_v38n4_387_f0006.png 이미지

Fig. 6. Normalized range error between true value and estimated value.

GOHHBH_2019_v38n4_387_f0007.png 이미지

Fig. 7. Normalized range RMSE for estimated target radiated noise.

Table 1. Mean and RMSE of normalized range error.

GOHHBH_2019_v38n4_387_t0001.png 이미지

Table 2. Range of SL below 10 % RMSE.

GOHHBH_2019_v38n4_387_t0002.png 이미지

참고문헌

  1. V. J. Aidala, "Kalman filter behavior in bearing-only tracking application," IEEE Trans. Aerosp. Electron. Syst, AES-15, 29-39 (1976). https://doi.org/10.1109/TAES.1979.308793
  2. J. M. Passerieux, D. Pillon, P. B. Benon, and C. Jauffret, "Target motion analysis with bearings and frequencies measurements," Proc. the 22nd Asilomar Conference, Pacific Glove, CA, 458-462 (1988).
  3. S. C. Nardone and V. J. Aidala, "Observability criteria for bearings-only target motion analysis," IEEE Trans. Aerosp. Electron. Syst. 17, 162-166 (1981).
  4. C. Jauffret and D. Pillon, "Observability in passive target motion analysis," IEEE Trans. Aerosp. Electron. Syst. 32, 1290-1300 (1996). https://doi.org/10.1109/7.543850
  5. M. H. Lee, J. H. Moon, I. S. Kim, C. S. Kim, and J. W. Choi, "Pre-processing faded measurements for bearingand- frequency target motion analysis," International J. Control, Automation, and Systems, 6, 424-433 (2008).
  6. I. S. Kim, "Study on bearing and frequency target motion analysis for passive line array sonar using accumulative batch estimation," J. Institute of Control, Robotics and Systems, 22, 788-796 (2016). https://doi.org/10.5302/J.ICROS.2016.16.0166
  7. S. Nardone, A. Lindgren, and K. Gong, "Fundamental properties and performance of conventional bearingsonly target motion analysis," IEEE Trans. Automatic Control, AC-29, 775-787 (1984).
  8. R. L. Streit and M. J. Walsh, "Bearing-only target motion analysis with acoustic propagation models of uncertain fidelity," IEEE Trans. Aerosp. Electron. Syst, 38, 1122-1137 (2002). https://doi.org/10.1109/TAES.2002.1145738
  9. R. L. Streit and T. E. Luginbuhl, "Maximum likelihood training of probabilistic neural networks," IEEE Trans. Neural Network, 5, 764-783 (1994). https://doi.org/10.1109/72.317728
  10. B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter : Particle Filter for Tracking Applications (Artech House, Boston.London, 2004), pp. 3-64.
  11. M. D. Collins, "User's Guide for RAM Version 1.0 and 1.0p," 1996.
  12. R. J. Urick, Principle of Underwater Sound, 3rd Ed. (McGraw-Hill, New York, 1983), pp. 17-30.