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Underwater Acoustic Research Trends with Machine Learning: Passive SONAR Applications

이근화|추영민|김국현|양해상
한국해양공학회 (The Korean Society of Ocean Engineers (KSOE))|2020.06|한국해양공학회지 (Journal of Ocean Engineering and Technology)|제34권 제3호|pp. 227-236|DOI 10.26748/KSOE.2020.017

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키워드

Machine learningDeep learningSignal processingUnderwater acousticsPassive SONAR systemPassive target classificationPassive source localization
주제분류해양공학

초록

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.

Abstract

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.

참고문헌 (43)

  1. Allen, N., "Performances of Human Listeners and an Automatic Aural Classifier in Discriminating between Sonar Target Echoes and Clutter", The Journal of the Acoustical Society of America, 130(3), pp.1287~1298 (2011)[DOI]
  2. Chakrabarty, S., "Broadband DOA Estimation Using Convolutional Neural Networks Trained with Noise Signals", Paper Presented at the 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (2017)
  3. Chi, J., "Sound Source Ranging Using a Feed-forward Neural Network with Fitting-based Early Stopping", The Journal of the Acoustical Society of America, 146(3), pp.EL258~EL264 (2019)[DOI]
  4. Choi, J., "Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning", Sensors, 19(16), pp.3492~ (2019)[DOI]
  5. Conan, E., "Source Depth Discrimination with a Vertical Line Array", The Journal of the Acoustical Society of America, 140(5), pp.EL434~EL440 (2016)[DOI]
  6. Conan, E., "Using the Trapped Energy Ratio for Source Depth Discrimination with a Horizontal Line Array : Theory and Experimental Results", The Journal of the Acoustical Society of America, 142(5), pp.2776~2786 (2017)[DOI]
  7. Das, A., "Theoretical and Experimental Comparison of Off-grid Sparse Bayesian Direction-of-arrival Estimation Algorithms", IEEE Access, 5, pp.18075~18087 (2017)[DOI]
  8. Das, A., "Narrowband and Wideband Off-grid Direction-of-arrival Estimation via Sparse Bayesian Learning", IEEE Journal of Oceanic Engineering, 43(1), pp.108~118 (2017)[DOI]
  9. Donoho, D. L., "Compressed Sensing", IEEE Transactions on Information theory, 52(4), pp.1289~1306 (2006)[DOI]
  10. Edelmann, G. F., "Beamforming Using Compressive Sensing", The Journal of the Acoustical Society of America, 130(4), pp.EL232~EL237 (2011)[DOI]
  11. Gemba, K. L., "Robust Ocean Acoustic Localization with Sparse Bayesian Learning", IEEE Journal of Selected Topics in Signal Processing, 13(1), pp.49~60 (2019)[DOI]
  12. Gerstoft, P., "Multisnapshot Sparse Bayesian Learning for DOA", IEEE Signal Processing Letters, 23(10), pp.1469~1473 (2016)[DOI]
  13. Gerstoft, P., "DOA Estimation in Heteroscedastic Noise", Signal Processing, 161, pp.63~73 (2019)[DOI]
  14. Hemminger, T. L., "Detection and Classification of Underwater Acoustic Transients Using Neural Networks", IEEE Transactions on Neural Networks, 5(5), pp.712~718 (1994)[DOI]
  15. Huang, Z., "Source Localization Using Deep Neural Networks in a Shallow Water Environment", The Journal of the Acoustical Society of America, 143(5), pp.2922~2932 (2018)[DOI]
  16. Jensen, F. B., "Computational Ocean Acoustics" (2011), Springer Science & Business Media
  17. Ke, X., "Underwater Acoustic Target Recognition Based on Supervised Feature-Separation Algorithm", Sensors, 18(12), pp.4318~ (2018)[DOI]
  18. Komari Alaie, H., "Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold", Applied Sciences, 8(1), pp.61~ (2018)[DOI]
  19. Lefort, R., "Direct Regressions for Underwater Acoustic Source Localization in Fluctuating Oceans", Applied Acoustics, 116, pp.303~310 (2017)[DOI]
  20. Liang, G., "Depth Discrimination for Low-Frequency Sources Using a Horizontal Line Array of Acoustic Vector Sensors Based on Mode Extraction", Sensors, 18(11), pp.3692~ (2018)[DOI]
  21. Murphy, S. M., "Examining the Robustness of Automated Aural Classification of Active Sonar Echoes", The Journal of the Acoustical Society of America, 135(2), pp.626~636 (2014)[DOI]
  22. Nannuru, S., "Sparse Bayesian Learning with Multiple Dictionaries", Signal Processing, 159, pp.159~170 (2019)[DOI]
  23. Nielsen, R. O., "Sonar Signal Processing" (1991), Artech House
  24. Niu, H., "Deep-learning Source Localization Using Multi-Frequency Magnitude-only Data", The Journal of the Acoustical Society of America, 146(1), pp.211~222 (2019)[DOI]
  25. Niu, H., "Ship Localization in Santa Barbara Channel Using Machine Learning Classifiers", The Journal of the Acoustical Society of America, 142(5), pp.EL455~EL460 (2017)[DOI]
  26. Niu, H., "Source Localization in an Ocean Waveguide Using Supervised Machine Learning", The Journal of the Acoustical Society of America, 142(3), pp.1176~1188 (2017)[DOI]
  27. Ozard, J. M., "An Artificial Neural Network for Range and Depth Discrimination in Matched Field Processing", The Journal of the Acoustical Society of America, 90(5), pp.2658~2663 (1991)[DOI]
  28. Park, Y., "Compressive Time Delay Estimation off the Grid", The Journal of the Acoustical Society of America, 141(6), pp.EL585~EL591 (2017)[DOI]
  29. Shin, F. B., "Full‐spectrum Signal Processing Using a Classify‐before‐detect Paradigm", The Journal of the Acoustical Society of America, 99(4), pp.2188~2197 (1996)[DOI]
  30. Tipping, M. E., "Sparse Bayesian Learning and the Relevance Vector Machine", Journal of Machine Learning Research, 1, pp.211~244 (2001)
  31. Tucker, S., "Classification of Transient Sonar Sounds Using Perceptually Motivated Features", IEEE Journal of Oceanic Engineering, 30(3), pp.588~600 (2005)[DOI]
  32. Wang, W., "Deep Transfer Learning for Source Ranging : Deep-sea Experiment Results", The Journal of the Acoustical Society of America, 146(4), pp.EL317~EL322 (2019)[DOI]
  33. Wang, X., "Underwater Acoustic Target Recognition : A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network", Remote Sensing, 11(16), pp.1888~ (2019)[DOI]
  34. Wang, Y., "Underwater Acoustic Source Localization Using Generalized Regression Neural Network", The Journal of the Acoustical Society of America, 143(4), pp.2321~2331 (2018)[DOI]
  35. Xenaki, A., "Grid-free Compressive Beamforming", The Journal of the Acoustical Society of America, 137(4), pp.1923~1935 (2015)[DOI]
  36. Xenaki, A., "Compressive Beamforming", The Journal of the Acoustical Society of America, 136(1), pp.260~271 (2014)[DOI]
  37. 양해상, "Underwater Acoustic Research Trends with Machine Learning: General Background", 한국해양공학회지, 34(2), pp.147~154 (2020), 한국해양공학회[DOI]
  38. Yang, H., "Competitive Deep-belief Networks for Underwater Acoustic Target recognition", Sensors, 18(4), pp.952~ (2018)[DOI]
  39. Yang, L., "Performance and Strategy Comparisons of Human Listeners and Logistic Regression in Discriminating Underwater Targets", The Journal of the Acoustical Society of America, 138(5), pp.3138~3147 (2015)[DOI]
  40. Young, V. W., "Perception-based Automatic Classification of Impulsive-source Active Sonar Echoes", The Journal of the Acoustical Society of America, 122(3), pp.1502~1517 (2007)[DOI]
  41. Zhang, Z., "Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning", IEEE Journal of Selected Topics in Signal Processing, 5(5), pp.912~926 (2011)[DOI]
  42. Zhang, Z., "Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-block Correlation", IEEE Transactions on Signal Processing, 61(8), pp.2009~2015 (2013)[DOI]
  43. Zion, B., "A Neural Network Approach to Source Localization", The Journal of the Acoustical Society of America, 90(4), pp.2081~2090 (1991)[DOI]

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