The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
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A study on DEMONgram frequency line extraction method using deep learning

딥러닝을 이용한 DEMON 그램 주파수선 추출 기법 연구

  • 신원식 (한양대학교 ERICA 지능정보융합공학과) ;
  • 권혁종 (국방기술진흥연구소) ;
  • 설호석 (한양대학교 ERICA 해양융합과학과) ;
  • 신원 (한양대학교 ERICA 전자공학과) ;
  • 고현석 (한양대학교 ERICA 지능정보융합공학과) ;
  • 송택렬 (한양대학교 ERICA 전자공학부) ;
  • 김다솔 (LIG넥스원(주)) ;
  • 최강훈 (LIG넥스원(주)) ;
  • 최지웅 (한양대학교 ERICA 지능정보융합공학과)
  • Received : 2023.09.04
  • Accepted : 2023.11.24
  • Published : 2024.01.31
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Abstract

Ship-radiated noise received by passive sonar that can measure underwater noise can be identified and classified ship using Detection of Envelope Modulation on Noise (DEMON) analysis. However, in a low Signal-to-Noise Ratio (SNR) environment, it is difficult to analyze and identify the target frequency line containing ship information in the DEMONgram. In this paper, we conducted a study to extract target frequency lines using semantic segmentation among deep learning techniques for more accurate target identification in a low SNR environment. The semantic segmentation models U-Net, UNet++, and DeepLabv3+ were trained and evaluated using simulated DEMONgram data generated by changing SNR and fundamental frequency, and the DEMONgram prediction performance of DeepShip, a dataset of ship-radiated noise recordings on the strait of Georgia in Canada, was compared using the trained models. As a result of evaluating the trained model with the simulated DEMONgram, it was confirmed that U-Net had the highest performance and that it was possible to extract the target frequency line of the DEMONgram made by DeepShip to some extent.

수중 소음 측정이 가능한 수동 소나에 수신된 선박 방사소음은 Detection of Envelope Modulation on Noise(DEMON) 분석으로 얻은 선박 정보를 사용하여 선박 식별과 분류가 가능하다. 하지만 낮은 신호대잡음비(Signal-to-Noise Ratio, SNR) 환경에서는 DEMON 그램 내 선박 정보가 담겨있는 표적 주파수선을 분석 및 파악하는데 어려움이 발생한다. 본 논문에서는 낮은 SNR 환경에서 보다 정확한 표적 식별을 위해 딥러닝 기법 중 의미론적 분할을 사용하여 표적 주파수선들을 추출하는 연구를 수행하였다. SNR과 기본 주파수를 변경시키며 생성한 모의 DEMON 그램 데이터를 사용하여 의미론적 분할 모델인 U-Net, UNet++, DeepLabv3+를 학습 후 평가하였고, 학습된 모델들을 이용하여 캐나다 조지아 해협에서 측정한 선박 방사소음 데이터셋인 DeepShip으로 제작한 DEMON 그램 예측 성능을 비교하였다. 모의 DEMON 그램으로 학습된 모델을 평가한 결과 U-Net이 성능이 가장 높았으며, DeepShip으로 만든 DEMON 그램의 표적 주파수선을 어느 정도 추출할 수 있는 것을 확인하였다.

Keywords

Acknowledgement

본 논문은 잠수함용 지능형 임무지원시스템 통합자동화 기술 사업을 통해 수행된 연구입니다. (계약번호 : KRIT-CT-22-023-01)

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