Underwater Acoustic Research Trends with Machine Learning: Active SONAR Applications
2
조회
0
다운로드
3
인용
0
참고문헌
키워드
초록
Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.
Abstract
Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.
참고문헌 (25)
- Abraham, D. A., "Underwater Acoustic Signal Processing:Modeling, Detection, and Estimation" (2019), Springer
- Abraham, D. A., "Active Sonar Detection in Shallow Water Using the Page Test", IEEE Journal of Oceanic Engineering, 27(1), pp.35~46 (2002)[DOI]
- 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]
- Azimi-Sadjadi, M. R., "Underwater Target Classification Using Wavelet Packets and Neural Networks", IEEE Transactions on Neural Networks, 11(3), pp.784~794 (2000)[DOI]
- Azimi-Sadjadi, M. R., "Underwater Target Classification in Changing Environments Using an Adaptive Feature Mapping", IEEE Transactions on Neural Networks, 13(5), pp.1099~1111 (2002)[DOI]
- Fischell, E. M., "Classification of Underwater Targets from Autonomous Underwater Vehicle Sampled Bistatic Acoustic Scattered Fields", The Journal of the Acoustical Society of America, 138(6), pp.3773~3784 (2015)[DOI]
- Fischell, E. M., "Environmental Effects on Seabed Object Bistatic Scattering Classification", The Journal of the Acoustical Society of America, 141(1), pp.28~37 (2017)[DOI]
- Fischell, E. M., "Supervised Machine Learning for Estimation of Target Aspect Angle from Bistatic Acoustic Scattering", IEEE Journal of Oceanic Engineering, 42(4), pp.759~769 (2017)[DOI]
- Gelb, J. M., "Statistics of Distinct Clutter Classes in Midfrequency Active Sonar", IEEE Journal of Oceanic Engineering, 35(2), pp.220~229 (2010)[DOI]
- Gorman, R. P., "Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets", Neural Networks, 1(1), pp.75~89 (1988)[DOI]
- Gorman, R. P., "Learned Classification of Sonar Targets Using a Massively Parallel Network", IEEE Transactions on Acoustics, Speech, and Signal Processing, 36(7), pp.1135~1140 (1988)[DOI]
- Isaacs, J.C., "Sonar Automatic Target Recognition for Underwater UXO Remediation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Workshops, pp.134~140 (2015)
- Kriminger, E., "Online Active Learning for Automatic Target Recognition", IEEE Journal of Oceanic Engineering, 40(3), pp.583~591 (2015)[DOI]
- Li, D., "Comparison of Different Classification Algorithms for Underwater Target Discrimination", IEEE Transactions on Neural Networks, 15(1), pp.189~194 (2004)[DOI]
- 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]
- Myers, V., "A Template Matching Procedure for Automatic Target Recognition in Synthetic Aperture Sonar Imager", IEEE Signal Processing Letters, 17(7), pp.683~686 (2010)[DOI]
- Runkle, P., "Hidden Markov Models for Multi-aspect Target Identification", IEEE Transactions on Signal Processing, 47(7), pp.2035~2040 (1999)[DOI]
- Runkle, P., "Multiaspect Identification of Submerged Elastic Targets via Wave-based Matching Pursuits and Hidden Markov Models", The Journal of the Acoustical Society of America, 106, pp.605~616 (1999)[DOI]
- Shin, F. B., "Active Impulsive Echo Discrimination in Shallow Water by Mapping Target Physicsderived Features to Classifiers", IEEE Journal of Oceanic Engineering, 22(1), pp.66~80 (1997)[DOI]
- Trucco, A., "Detection of Objects Buried in the Seafloor by a Pattern Recognition Approach", IEEE Journal of Oceanic Engineering, 26(4), pp.769~782 (2001)[DOI]
- Trucco, A., "Acoustic Detection of Objects Buried in the Seafloor", Electronics Letters, 36(18), pp.1595~1596 (2000)[DOI]
- 양해상, "Underwater Acoustic Research Trends with Machine Learning: General Background", 한국해양공학회지, 34(2), pp.147~154 (2020), 한국해양공학회[DOI]
- 양해상, "Underwater Acoustic Research Trends with Machine Learning: Passive SONAR Applications", 한국해양공학회지, 34(3), pp.227~236 (2020), 한국해양공학회[DOI]
- Yao, D., "A Study of Effects of Sonar Bandwidth for Underwater Target Classification", IEEE Journal of Oceanic Engineering, 27(3), pp.619~627 (2002)[DOI]
- 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]