본문 바로가기
KERD
논문KCI 등재

Snapping shrimp noise detection and mitigation for underwater acoustic orthogonal frequency division multiple communication using multilayer frequency

2

조회

0

다운로드

1

인용

0

참고문헌

키워드

주제분류조선공학

초록

This paper proposes Snapping Shrimp Noise (SSN) detection and corrupted Orthogonal Frequency Division Multiplexing (OFDM) reconstruction methods to increase Bit Error Rate (BER) performance when OFDM transmitted signal is corrupted by impulsive SSNs in underwater acoustic communications. The proposed detection method utilizes multilayer wavelet packet decomposition for detecting impulsive and irregularly concentrated and SSN energy in specific frequency bands of SSN, and the proposed reconstruction scheme uses iterative decision directed-subcarrier reconstruction to recover corrupted OFDM signals using multiple carrier characteristics. Computer simulations were executed to show receiver operating characteristics curve for the detection performance and BER for the reconstruction. The practical ocean experiment of SAVEX 15 demonstrated that the proposed method exhibits a better detection performance compared with conventional detection method and improves BER by 250% and 1230% for uncoded and coded data, respectively, compared with the conventional reconstruction scheme.

Abstract

This paper proposes Snapping Shrimp Noise (SSN) detection and corrupted Orthogonal Frequency Division Multiplexing (OFDM) reconstruction methods to increase Bit Error Rate (BER) performance when OFDM transmitted signal is corrupted by impulsive SSNs in underwater acoustic communications. The proposed detection method utilizes multilayer wavelet packet decomposition for detecting impulsive and irregularly concentrated and SSN energy in specific frequency bands of SSN, and the proposed reconstruction scheme uses iterative decision directed-subcarrier reconstruction to recover corrupted OFDM signals using multiple carrier characteristics. Computer simulations were executed to show receiver operating characteristics curve for the detection performance and BER for the reconstruction. The practical ocean experiment of SAVEX 15 demonstrated that the proposed method exhibits a better detection performance compared with conventional detection method and improves BER by 250% and 1230% for uncoded and coded data, respectively, compared with the conventional reconstruction scheme.

참고문헌 (35)

  1. Ahmed O. Abdul Salam, "Adaptive threshold and optimal frame duration for multi-taper spectrum sensing in cognitive radio", ICT Express, 5(1), pp.31~36 (2019), 한국통신학회[DOI]
  2. Ahn, J. M., "Signal feature extraction and detection for snapping shrimp noise", Proc. International Symposium on UltraSonic Electronics (2017)
  3. Anastassopouios, V., "A new and robust CFAR detection algorithm", Trans. IEEE Aeropsp. Electron. Syst, 28(2), pp.420~427 (1992)
  4. Bertilone, D. C., "Statistics of biological noise and performance of generalized energy detectors for passive detection", J. Ocean. Eng, 26(2), pp.285~293 (2001)
  5. Bohnenstienhl, D. R., "The cousrious acoustic behaviour of estuarine snapping shrimp: temporal patterns of snapping shrimp sound in sub-tidal oyster reef habitat", PLoS One, 11(1) (2016)
  6. Chitre, M., "Performance of coded OFDM in very shallow water channels and snapping shrimp noise", Proc. Oceans (2005)
  7. Chitre, M. A., "Optimal and near optimal signal detection in snapping shrimp dominated ambient noise", J. IEEE Ocean. Eng, 31(2), pp.497~503 (2006)
  8. Daubechies, I., "The wavelet transform, time frequency localization and signal analysis", Trans. IEEE Inf. Theory, 36(5), pp.961~1005 (1990)
  9. Dron, J. P., "Improvement of sensitivity of the scalar indicator (crest factor, kurtosis) using a denoising method by spectral subtraction: application to the detection of defects in ball bearings", J. Sound Vib, 270(1-2), pp.61~73 (2004)
  10. Gandhi, P. P., "Optimality of the cell averaging CFAR detector", Trans. IEEE Inf. Theory, 40(4), pp.1226~1228 (1994)
  11. Guimar~aes, D. A., "Snapping shrimp noise reduction using convex optimization for underwater acoustic communication in warm shallow water", Proc. International Telecommunications Symposium (2014)
  12. Kim, B. N., "Snapping shrimp sound measured under laboratory conditions", J. Jpn. J. Appl. Phys, 49(7S) (2010)
  13. Kim, B. N., "11. acoustic characteristics of the snapping shrimp sound observed in the coastal sea of Korea", J. Jpn. Appl. Phys, 50(7S) (2011)
  14. Kim, B. N., "Long-term Observation of Underwater Ambient Noise at the Ieodo Ocean Research Station in Korea", Proc. IEEE Underwater Technology 2015 (2015)
  15. Kim, H. S., "Snapping shrimp noise mitigation based on statistical detection in underwater acoustic orthogonal frequency division multiplexing systems", J. Jpn. J. Appl. Pys, 56(7S1) (2017)
  16. Kuai, X., "Impulsive noise mitigation in underwater acoustic OFDM systems", Trans. IEEE Veh. Tech, 65(10), pp.8190~8202 (2016)
  17. 이호준, "주파수 도약 확산 스펙트럼 시스템에서 CFAR 검출기를 이용한 항재밍 영향 성능 분석", 한국통신학회논문지, 42(11), pp.2069~2078 (2017), 한국통신학회[DOI]
  18. Legg, M. W., "Analysis of impulsive biological noise due to snapping shrimp as a point process in time", Proc. Oceans (2007)
  19. Mahmood, A., "Ambient noise in warm shallow water : a communications perspective", J. IEEE Commun, 55(6), pp.198~204 (2017)
  20. Mallat, S. G., "A theory for multiresolution signal decomposition the wavelet representation", Trans. IEEE Pattern Anal. Mach. Intell, 11(7) (1989)
  21. Mallat, S., "A Wavelet Tour of Signal Processing" (1999), Academic Press
  22. Mcaulay, R. J., "A decision directed adaptive tracker", Trans. IEEE Aerosp. Electron. Syst, 9(2), pp.229~236 (1973)
  23. Nowlan, S. J., "A soft decision-directed LMS algorithm for blind equalization", Trans. IEEE Commun, 41(2), pp.275~279 (1993)
  24. Pachaud, C., "Crest factor and curtosis contributions to indentify defects inducing periodical impulsive forces", J. Mech. Syst. Signal Process, 11(6), pp.903~916 (1997)
  25. Picchi, G., "Blind equalization and carrier recovery using stop-and-go decision directed algorithm", Trans. IEEE Commun, 35(9), pp.877~887 (1987)
  26. Qian, X., "The signal structure analysis of non-Gaussian noise based on wavelet packet transform", J. Electron. Inf. Technol, 26, pp.61~62 (2004)
  27. Rioul, O., "Wavelets and signal processing", J. IEEE Signal Process, 8(4), pp.14~38 (1991)
  28. Shi, K., "Decision directed fine synchronization in OFDM systems", Trans. IEEE Commun, 53(3), pp.408~412 (2005)
  29. Singh, O., "A robust R-peak detection algorithm using wavelet packet", J. Int. Comput. Appl, 36(5), pp.37~43 (2011)
  30. Son, S. U., "Measurements of high frequency acoustic transmission loss during SAVEX' 15", J. Acoust. Soc. Am, 140(4) (2016)
  31. Tewfik, A. H., "On the optimal choice of a wavelet for signal representation", Trans. IEEE Inf. Theory, 38(2), pp.747~765 (1992)
  32. Versluis, M., "How snapping shrimp snap : Through cavitating bubbles", J. Sci, 289(5487), pp.2114~2117 (2000)
  33. Wang, D., "Best basis based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection", J. Expert Syst. Appl, 38(11), pp.14314~14320 (2011)
  34. Whitlow, W. L. A., "The acoustics of the snapping shrimp synalpheus parneomeris in Kaneohe bay", J. Acoust. Soc. Am, 103(1), pp.41~47 (1998)
  35. Wu, Y., "Feature extraction and assessment using wavelet packets for monitoring of machining processes", J. Mech. Syst. Signal Process, 10(1), pp.29~53 (1996)

관련 논문AI추천

이 간행물의 다른 논문 (80건)