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Open AccessArticle

Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning

Department of Defense Systems Engineering, Sejong University, Neungdong-ro 209, Kwangjin-gu, Seoul 05006, Korea
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Sensors 2019, 19(16), 3492; https://doi.org/10.3390/s19163492
Received: 27 June 2019 / Revised: 26 July 2019 / Accepted: 7 August 2019 / Published: 9 August 2019
Four data-driven methods—random forest (RF), support vector machine (SVM), feed-forward neural network (FNN), and convolutional neural network (CNN)—are applied to discriminate surface and underwater vessels in the ocean using low-frequency acoustic pressure data. Acoustic data are modeled considering a vertical line array by a Monte Carlo simulation using the underwater acoustic propagation model, KRAKEN, in the ocean environment of East Sea in Korea. The raw data are preprocessed and reorganized into the phone-space cross-spectral density matrix (pCSDM) and mode-space cross-spectral density matrix (mCSDM). Two additional matrices are generated using the absolute values of matrix elements in each CSDM. Each of these four matrices is used as input data for supervised machine learning. Binary classification is performed by using RF, SVM, FNN, and CNN, and the obtained results are compared. All machine-learning algorithms show an accuracy of >95% for three types of input data—the pCSDM, mCSDM, and mCSDM with the absolute matrix elements. The CNN is the best in terms of low percent error. In particular, the result using the complex pCSDM is encouraging because these data-driven methods inherently do not require environmental information. This work demonstrates the potential of machine learning to discriminate between surface and underwater vessels in the ocean. View Full-Text
Keywords: target depth classification; machine learning; random forest; support vector machine; feed-forward neural network; convolutional neural network; cross-spectral density matrix; vertical line array target depth classification; machine learning; random forest; support vector machine; feed-forward neural network; convolutional neural network; cross-spectral density matrix; vertical line array
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Choi, J.; Choo, Y.; Lee, K. Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning. Sensors 2019, 19, 3492.

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