DOI QR코드

DOI QR Code

Time delay estimation between two receivers using weighted dictionary method for active sonar

능동소나를 위한 가중 딕션너리를 사용한 두 수신기 간 신호 지연 추정 방법

  • Received : 2021.07.02
  • Accepted : 2021.08.23
  • Published : 2021.09.30

Abstract

In active sonar, time delay estimation is used to find the distance between the target and the sonar. Among the time delay estimation methods for active sonar, estimation in the frequency domain is widely used. When estimating in the frequency domain, the time delay can be thought of as a frequency estimator, so it can be used relatively easily. However, this method is prone to rapid increase in error due to noise. In this paper, we propose a new method which applies weighted dictionary and sparsity in order to reduce this error increase and we extend it to two receivers to propose an algorithm for estimating the time delay between two receivers. And the case of applying the proposed method and the case of not applying the proposed method including the conventional frequency domain algorithm and Generalized Cross Correlation-Phase transform (GCC-PHAT) in a white noise environment were compared with one another. And we show that the newly proposed method has a performance gain of about 15 dB to about 60 dB compared to other algorithms.

능동 소나에서 시간 지연 추정은 목표와 소나 사이의 거리를 알아내기 위해서 사용하고 있다. 능동 소나에서 시간 지연을 추정할 때 주파수 영역에서 계산하면 시간 지연 추정이 주파수 추정으로 바꾸어 생각할 수 있어서 비교적 쉽게 사용할 수 있다. 그러나 이 방법은 잡음에 의해 오류가 급증할 요소가 포함되어 있다. 본 논문에서는 이런 오류 급증 현상을 줄일 수 있는 가중 딕션너리를 사용하는 희소성 기반 추정 방법을 새롭게 제안한다. 또 이 방법을 두 개의 수신기로 확대 적용하여 두 수신기 간 시간 지연을 추정하는 알고리즘을 제안한다. 그리고 백색 잡음 환경에서 제안한 방법을 적용한 것과 일반 상호 상관 알고리즘(Generalized Cross Correlation-Phase transform, GCC-PHAT) 및 일반 주파수 영역 방법을 포함한 제안한 방법을 적용하지 않은 방법들을 서로 비교한다. 그리고 새로 제안한 방법이 다른 비교 대상 알고리즘들보다 약 15 dB에서 약 60 dB의 성능 이득이 있음을 보인다.

Keywords

Acknowledgement

본 연구는 국방과학연구소의 지원을 받아 수행되었음(UD190005DD).

References

  1. E. Tiana-Roig, F. Jacobsen, and E. Grande, "Beam-forming with a circular microphone array for localization of environmental noise sources," J Acoust Soc. Am. 128, 3535-3542 (2010). https://doi.org/10.1121/1.3500669
  2. J. Lim,, Y. Pyeon, S. Lee, and M. Cheong, "A time delay estimation method using canonical correlation analysis and log-sum regularization" (in Korean), J. Acoust. Soc. Kr. 36, 279-284 (2017).
  3. J. Lim, M. Cheong, and S. Kim, "Improved generalized cross correlation-phase transform based time delay estimation by frequency domain autocorrelation" (in Korean), J. Acoust. Soc. Kr. 37, 271-275 (2018).
  4. J. Huang, T. Supaongprapa, I. Terakura, F. Wang, N. Ohnishi, and N. Sugie, "A model based sound localization system and its application to robot navigation," Robotics and Autonomous Systems, 27, 199-209 (1999). https://doi.org/10.1016/S0921-8890(99)00002-0
  5. S. A. R. Zekavat and R. M. Buehrer, "Theory, practice and advances" in Handbook of Position Location: Theory, edited by S.A.Zekavat (Wiley-IEEE Press, Hoboken, NJ, 2019).
  6. A. D. Waite, Sonar for Practising Engineers, 3rd Ed (Wiley, Hoboken, NJ, 2002), pp. 161-220.
  7. Y. Zhang, Q. Liu, R. Hong, P. Pan, and Zhen-Miao Deng, "A novel monopulse angle estimation method for wideband LFM radars," Sensors, 16, 1-13 (2016). https://doi.org/10.1109/JSEN.2015.2493739
  8. M. Pallas and G. Jourdain, "Active high resolution time delay estimation for large BT signals ," IEEE SP. 39, 781-788 (1991).
  9. P. Knee, Sparse Representations for Radar with MATLAB® Examples (Morgan & Laypool publishers, San Rafael, CA, 2012), pp. 8-13.
  10. J. S. Lim and M. Jung, "Time delay estimation between two receivers using basis pursuit denoising" (in Korean), J. Acoust. Soc. Kr. 36, 285-291 (2017).
  11. E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory, 52, 489-509 (2006). https://doi.org/10.1109/TIT.2005.862083
  12. The MOSEK Optimization Tools Version 2.5. User's Manual and Reference, http://www.mosek.com, (Last viewed May 1, 2021).
  13. PDCO: Primal-Dual Interior Method for Convex Objectives, http://www.stanford.edu/group/SOL/software/pdco.html, (Last viewed May 1, 2021).
  14. SPGL1, A Solver for Large Scale Sparse Reconstruction, http://www.cs.ubc.ca/labs/scl/spgl1/, (Last viewed May 1, 2021).