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http://dx.doi.org/10.1016/j.ijnaoe.2019.10.004

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

Ahn, Jongmin (Dept. of Electronic Engineering, INHA)
Lee, Hojun (Dept. of Electronic Engineering, INHA)
Kim, Yongcheol (Dept. of Electronic Engineering, INHA)
Chung, Jeahak (Dept. of Electronic Engineering, INHA)
Publication Information
International Journal of Naval Architecture and Ocean Engineering / v.12, no.1, 2020 , pp. 258-269 More about this Journal
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.
Keywords
Snapping shrimp; Noise mitigation; Signal reconstruction; Underwater acoustic; OFDM;
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1 Guimaraes, D.A., Chaves, L.S., Souza, R.a A. A. de, 2014. Snapping shrimp noise - reduction using convex optimization for underwater acoustic communication in warm shallow water. In: Proc. International Telecommunications Symposium, Sao Paulo, August.
2 Kim, B.N., Hahn, J.Y., Choi, B.K., Kim, B.C., 2010. Snapping shrimp sound measured under laboratory conditions. J. Jpn. J. Appl. Phys. 49 (7S).
3 Kim, B.N., Hahn, J.Y., Choi, B.K., Kim, B.C., Par, Y., Jung, S.K., Lee, Y.K., 2011. 11. acoustic characteristics of the snapping shrimp sound observed in the coastal sea of Korea. J. Jpn. Appl. Phys. 50 (7S).
4 Kim, B.N., Jung, S.K., Choi, B.K., Bong-Chae, K.M., Shim, J.S., 2015. Long-term Observation of Underwater Ambient Noise at the Ieodo Ocean Research Station in Korea. In: Proc. IEEE Underwater Technology 2015. Chennai, February.
5 Kim, H.S., Seo, J.P., Ahn, J.M., Chung, J.H., 2017. Snapping shrimp noise mitigation based on statistical detection in underwater acoustic orthogonal frequency division multiplexing systems. J. Jpn. J. Appl. Pys. 56 (7S1).
6 Kuai, X., Sun, H., Zhou, S., 2016. Impulsive noise mitigation in underwater acoustic OFDM systems. Trans. IEEE Veh. Tech. 65 (10), 8190-8202.   DOI
7 Lee, H.J., Kim, S.H., Chung, J.H., 2017. Performance Analysis of anti-jamming by CFAR detector in frequency hopping spread spectrum system. J. KICIS 42 (11), 2069-2078.   DOI
8 Legg, M.W., Ducan, A.J., Zaknich, A., 2007. Analysis of impulsive biological noise due to snapping shrimp as a point process in time. In: Proc. Oceans, Aberdeen. Jun.
9 Mahmood, A., Chritre, M., 2017. Ambient noise in warm shallow water: a communications perspective. J. IEEE Commun. 55 (6), 198-204.   DOI
10 Mallat, S.G., 1989. A theory for multiresolution signal decomposition the wavelet representation. Trans. IEEE Pattern Anal. Mach. Intell. 11 (7).
11 Bertilone, D.C., Killeen, D.S., 2001. Statistics of biological noise and performance of generalized energy detectors for passive detection. J. Ocean. Eng. 26 (2), 285-293.   DOI
12 Ahmed, O.A., Ray, E.S., Saleh, R.A., Kahtan, M., Qassim, N., 2019. Adaptive threshold and optimal frame duration for multi-taper spectrum sensing in cognitive radio. J. ICT Express 5 (1), 31-36.   DOI
13 Ahn, J.M., Kim, H.S., Chung, J.H., 2017. Signal feature extraction and detection for snapping shrimp noise. In: Proc. International Symposium on UltraSonic Electronics, endai, October.
14 Anastassopouios, V., Lampropoulos, G., 1992. A new and robust CFAR detection algorithm. Trans. IEEE Aeropsp. Electron. Syst. 28 (2), 420-427.   DOI
15 Bohnenstienhl, D.R., Ashlee, Lillis, Eggleston, D.B., 2016. The cousrious acoustic behaviour of estuarine snapping shrimp: temporal patterns of snapping shrimp sound in sub-tidal oyster reef habitat. PLoS One 11 (1).
16 Chitre, M., Potter, J.R., Ong, S.H., 2005. Performance of coded OFDM in very shallow water channels and snapping shrimp noise. In: Proc. Oceans. Washington, Sept.
17 Chitre, M.A., Potter, J.R., Ong, S.H., 2006. Optimal and near optimal signal detection in snapping shrimp dominated ambient noise. J. IEEE Ocean. Eng. 31 (2), 497-503.   DOI
18 Daubechies, I., 1990. The wavelet transform, time frequency localization and signal analysis. Trans. IEEE Inf. Theory 36 (5), 961-1005.   DOI
19 Dron, J.P., Bolaers, F., Rasolofondraibe, I., 2004. 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 (1e2), 61-73.   DOI
20 Gandhi, P.P., Kassam, S.A., 1994. Optimality of the cell averaging CFAR detector. Trans. IEEE Inf. Theory 40 (4), 1226-1228.   DOI
21 Rioul, O., Vetterli, M., 1991. Wavelets and signal processing. J. IEEE Signal Process. 8(4), 14-38.   DOI
22 Mcaulay, R.J., Denlinger, E., 1973. A decision directed adaptive tracker. Trans. IEEE Aerosp. Electron. Syst. 9 (2), 229-236.   DOI
23 Nowlan, S.J., Hinton, G.E., 1993. A soft decision-directed LMS algorithm for blind equalization. Trans. IEEE Commun. 41 (2), 275-279.   DOI
24 Pachaud, C., Salvetat, R., Fray, C., 1997. Crest factor and curtosis contributions to indentify defects inducing periodical impulsive forces. J. Mech. Syst. Signal Process. 11 (6), 903-916.   DOI
25 Picchi, G., Prati, G., 1987. Blind equalization and carrier recovery using stop-and-go decision directed algorithm. Trans. IEEE Commun. 35 (9), 877-887.   DOI
26 Qian, X., Chunguang, L., Shenguang, G., 2004. The signal structure analysis of nonGaussian noise based on wavelet packet transform. J. Electron. Inf. Technol. 26, 61-62.
27 Shi, K., Serpedin, E., Ciblat, P., 2005. Decision directed fine synchronization in OFDM systems. Trans. IEEE Commun. 53 (3), 408-412.   DOI
28 Mallat, S., 1999. A Wavelet Tour of Signal Processing, second ed. Academic Press, New York.
29 Singh, O., Sunkaria, R.K., 2011. A robust R-peak detection algorithm using wavelet packet. J. Int. Comput. Appl. 36 (5), 37-43.
30 Son, S.U., Choi, J.W., Lee, S.W., Nam, S.H., Cho, S.H., 2016. Measurements of high frequency acoustic transmission loss during SAVEX' 15. J. Acoust. Soc. Am. 140(4).
31 Tewfik, A.H., Sinha, D., Jorgensen, P., 1992. On the optimal choice of a wavelet for signal representation. Trans. IEEE Inf. Theory 38 (2), 747-765.   DOI
32 Versluis, M., Schemitz, B., Heydt, A., Lohse, D., 2000. How snapping shrimp snap: Through cavitating bubbles. J. Sci. 289 (5487), 2114-2117.
33 Wu, Y., Du, R., 1996. Feature extraction and assessment using wavelet packets for monitoring of machining processes. J. Mech. Syst. Signal Process. 10 (1), 29-53.   DOI
34 Wang, D., Miao, D., Xie, C., 2011. Best basis based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. J. Expert Syst. Appl. 38 (11), 14314-14320.
35 Whitlow, W.L.A., Banks, K., 1998. The acoustics of the snapping shrimp synalpheus parneomeris in Kaneohe bay. J. Acoust. Soc. Am. 103 (1), 41-47.   DOI