Adaptive Tow Ship Noise Cancellation Using Deep Regression Neural Network
Keywords:deep regression neural network, towed sensor array, tow ship noise cancellation, weight initialization
This paper investigates the problem of cancellation of noise generated by own platform in shallow water scenario. In the case of underwater acoustics, the target signal detection and tracking in the presence of tow ship noise is a challenging task. A computationally intensive technique is necessary for tow ship noise suppression. In this paper, an algorithm using deep regression neural network (DRNN) along with minimum variance distortionless response (MVDR) beamformer is presented for tow ship noise cancellation. Nine DRNN’s each with different weight initialization techniques and activation functions are designed for effective tow ship noise cancellation. The designed DRNNs is tested using the simulated data and further validated using the real data collected during the trials from Arabian Sea.
URICK, R.J. Principles of Underwater Sound. 3rd ed. Newport Beach: Peninsula Publishing, 1983. ISBN 978-0-932146-62-7.
LEMON, S.J. Towed-Array History, 1917-2003. IEEE Journal of Oceanic Engineering, 2004, 29(2), pp. 365-373. DOI 10.1109/JOE.2004.829791.
HINICH, M.J., D. MARANDINO and E.J. SULLIVAN. Bispectrum of Ship Radiated Noise. Journal of the Acoustical Society of America, 1989, 85(4), pp. 1512-1517. DOI 10.1121/1.397352.
BREKHOVSKIKH, L.M. and Yu.P. LYSANOV. Fundamentals of Ocean Acoustics. New York: Springer, 2003. ISBN 978-0-387-21655-3.
KUPERMAN, W.A. and J.F. LYNCH. Shallow-Water Acoustics. Physics Today, 2004, 57(10), pp.55-61. DOI 10.1063/1.1825269.
VACCARO, R.J., The Past, Present and Future of Underwater Acoustic Signal Processing. IEEE Signal Processing Magazine, 1998, 15(4), pp. 21-51. DOI 10.1109/79.689583.
LI, J. and P. STOICA. Robust Adaptive Beamforming. Hoboken: Wiley, 2005. ISBN 978-0-471-73346-6.
ROBERT, M.K. and S.P. BEERENS. Adaptive Beamforming Algorithms for Tow Ship Noise Cancelling. In: Conference Proceedings UDT Europe 2002. Swanley: Nexus Media, 2002.
VACCARO, R.J., A. CHHETRI and B.F. HARRISON. Matrix Filter Design for Passive SONAR Interference Suppression. Journal of the Acoustical Society of America, 2004, 115(6), pp. 3010-3020. DOI 10.1121/1.1736653.
KOGON, S.M. Robust Adaptive Beamforming for Passive Sonar using Eigenvector/Beam Association and Excision. In: Sensor Array and Multichannel Signal Processing Workshop Proceedings. Rosslyn: IEEE, 2002, pp. 33-37. DOI 10.1109/SAM.2002.1190994.
HARRISON, B.F. The Eigencomponent Association Method for Adaptive Interference Suppression. Journal of the Acoustical Society of America, 2004, 115(5), pp. 2122-2128. DOI 10.1121/1.1699395.
TUFTS, K.D. Adaptive Detection Using Low Rank Approximation to a Data Matrix. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(1), pp. 55-67. DOI 10.1109/7.250406.
SONG, Y., W. FAN and L. XU. Tow-Ship Interference Suppression Based on Blind Source Separation for Passive Sonar. In: 3rd International Symposium on Parallel Architectures, Algorithms and Programming. Liaoning: IEEE, 2010, pp. 426-430. DOI 10.1109/PAAP.2010.40.
ZHANG, B. Research on Directional Interference Cancelling. In: 3rd International Symposium on Intelligent Information Technology and Security Informatics. Jian: IEEE, 2010, pp. 549-552. DOI 10.1109/IITSI.2010.96.
LI, Y., C. SUN, H. YU and L. WANG. A Technique of Suppressing Towed Ship Noise. In: IEEE International Conference on Signal Processing, Communications and Computing. Xi’an: IEEE, 2011, pp. 1-4. DOI 10.1109/ICSPCC.2011.6061631.
FENG, J., N. ZOU, Y. WANG and Y. HAO. Methods of Suppressing Tow Ship Noise with a Horizontal Linear Array. Journal of the Acoustical Society of America, 2018, 143(3), pp. 3010-3020. DOI 10.1121/1.5036438.
SULLIVAN, E.J. and J.V. CANDY. Cancelling Tow Ship Noise Using an Adaptive Model-Based Approach. In: Proceedings of the IEEE/OES Eighth Working Conference on Current Measurement Technology. Southampton: IEEE, 2005. DOI 10.1109/CCM.2005.1506325.
MIO, K., Y. CHOCHEYRAS and Y. DOISY. Space Time Adaptive Processing for Low Frequency Sonar. In: OCEANS 2000 MTS/IEEE Conference and Exhibition. Conference Proceedings. Providence: IEEE, 2000. DOI 10.1109/OCEANS.2000.881786.
GUERCI, J.R., J.S. GOLDSTEIN and I.S. REED. Optimal and Adaptive Reduced-Rank STAP. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(2), pp. 647-663. DOI 10.1109/7.845255.
REMADEVI, M., N. SURESHKUMAR, R. RAJESH. and T. SANTHANAKRISHNAN. Cancellation of Towing Ship Interference in Passive SONAR in a Shallow Ocean Environment. Defence Science Journal, 2022, 72(1), pp. 122-132. DOI 10.14429/dsj.72.17370.
HINTON, G.E. and R. SALAKHUTDINOV. Reducing the Dimensionality of Data with Neural Networks. Science, 2006, 313(5786), pp. 504-507. DOI 10.1126/science.1127647.
DELIANG, W. and J. CHEN. Supervised Speech Separation Based on Deep Learning: An Overview. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018, 26(10), pp. 1702-1726. DOI 10.1109/TASLP.2018.2842159.
TU, Y., J. DU, Y. XU, L. DAI and C.-H. LEE. Speech Separation Based on Improved Deep Neural Networks with Dual Outputs of Speech Features for Both Target and Interfering Speakers. In: 9th International Symposium on Chinese Spoken Language Processing (ISCSLP). Singapore: IEEE, 2014. DOI 10.1109/ISCSLP.2014.6936615.
HINTON, G., L. DENG, D YU; G.E. DAHL, A. MOHAMED, N. JA, A. SENIOR, V. VANHOUCKE, P. NGUYEN, T.N. SAINATH and B. KINGSBURY. Deep Neural Networks for Acoustic Modelling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine, 2012, 29(6), pp. 82-97. DOI 10.1109/MSP.2012.2205597.
WANG, D.L., U. KJEMS, M.S. PEDERSEN, J.B. BOLDT and T. LUNNER. Speech Intelligibility in Background Noise with Ideal Binary Time-Frequency Masking. Journal of the Acoustical Society of America, 2009, 125(4), pp. 2336-2347. DOI 10.1121/1.3083233.
SALEEM, N., M. IRFAN, X. CHEN and M. ALI. Deep Neural Network Based Supervised Speech Enhancement in Speech Babble Noise. In: IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS). Singapore: IEEE, 2018. DOI 10.1109/ICIS.2018.8466542.
KASE, Y., T. NISHIMURA, T. OHGANE, Y. OGAWA, D. KITAYAMA and Y. KISHIYAMA. DOA Estimation of Two Targets with Deep Learning. In: 15th Workshop on Positioning, Navigation and Communications (WPNC). Bremen: IEEE, 2018. DOI 10.1109/WPNC.2018.8555814S.
CHAKRABARTY, S. and E.A.P. HABETS. Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals. IEEE Journal of Selected Topics in Signal Processing, 2019, 13(1), pp. 8-21. DOI 10.1109/JSTSP.2019.2901664.
LI, Q., X. ZHANG and H. LI. Online Direction of Arrival Estimation Based on Deep Learning. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Calgary: IEEE, 2018. DOI 10.1109/ICASSP.2018.8461386.
PEKERIS, C.L. Theory of Propagation of Sound in a Half-Space of Variable Sound Velocity under Conditions of Formation of a Shadow Zone. Journal of the Acoustical Society of America, 1946, 18(2), pp. 295-315. DOI 10.1121/1.1916366.
NAIR, B.M., K.P. ARUNKUMAR and S.B. MENON. Broadband Passive Sonar Signal Simulation in Shallow Ocean. Defence Science Journal, 2011, 61(4), pp. 370-376. DOI 10.14429/dsj.61.89.
SRIVASTAVA, N., G. HINTON, A. KRIZHEVSKY, I. SUTSKEVER and R. SALAKHUTDINOV. Dropout: A Simple Way to Prevent Neural Networks from Overfitting Journal of Machine Learning Research [online], 2014, 15(56), pp. 1929-1958 [viewed 2022-01-21]. Available from: https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
KINGMA, D.P. and J.L. BA. Adam: A Method for Stochastic Optimization. In: 3rd International Conference for Learning Representations. Ithaca: arXiv.org, 2014. DOI 10.48550/arXiv.1412.6980.
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