Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Active Sonar Target/Non-target Classification using Convolutional Neural Networks

CNN을 이용한 능동 소나 표적/비표적 분류

  • Kim, Dongwook (School of Electronics Eng., Graduate School, Kyungpook National University) ;
  • Seok, Jongwon (Dept. of Information & Communication Eng., Changwon National University) ;
  • Bae, Keunsung (School of Electronics Eng., Graduate School, Kyungpook National University)
  • Received : 2018.05.15
  • Accepted : 2018.07.19
  • Published : 2018.09.30
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Abstract

Conventional active sonar technology has relied heavily on the hearing of sonar operator, but recently, many techniques for automatic detection and classification have been studied. In this paper, we extract the image data from the spectrogram of the active sonar signal and classify the extracted data using CNN(convolutional neural networks), which has recently presented excellent performance improvement in the field of pattern recognition. First, we divided entire data set into eight classes depending on the ratio containing the target. Then, experiments were conducted to classify the eight classes data using proposed CNN structure, and the results were analyzed.

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References

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