Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Analysis of Features and Discriminability of Transient Signals for a Shallow Water Ambient Noise Environment

천해 배경잡음 환경에 적합한 과도신호의 특징 및 변별력 분석

  • Lee, Jaeil (Dept. of Ocean System Engineering, Jeju Nat'l University) ;
  • Kang, Youn Joung (Dept. of Ocean System Engineering, Jeju Nat'l University) ;
  • Lee, Chong Hyun (Dept. of Ocean System Engineering, Jeju Nat'l University) ;
  • Lee, Seung Woo (Sonar Systems PMO, Agency for Defense Development) ;
  • Bae, Jinho (Dept. of Ocean System Engineering, Jeju Nat'l University)
  • 이재일 (제주대학교 해양시스템공학과) ;
  • 강윤정 (제주대학교 해양시스템공학과) ;
  • 이종현 (제주대학교 해양시스템공학과) ;
  • 이승우 (국방과학연구소 소나체계개발단) ;
  • 배진호 (제주대학교 해양시스템공학과)
  • Received : 2014.01.10
  • Accepted : 2014.06.26
  • Published : 2014.07.25
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Abstract

In this paper, we analyze the discriminability of features for the classification of transient signals with an ambient noise in a shallow water. For the classification of the transient signals, robust features for the variance of a noise are required due to a low SNR under a marine environment. In the modelling the ambient noise in shallow water, theoretical noise model, Wenz's observation data from the shallow water, and Yule-walker filter are used. Discrimination of each feature of the transient signals with an additive ambient noise is analyzed by utilizing a Fisher score. As the analysis of a classification accuracy about the transient signals of 24 classes using the selected features with a high discriminability, the features selected in the environment without a noise relatively have a good classification accuracy. From the analyzed results, we finally select a total 16 features out of 28 features. The recognition using the selected features results in the classification accuracy of 92% in SNR 20dB using Multi-class SVM.

본 논문에서는 천해 배경잡음 환경에서 과도신호 분류에 적합한 특징 선택을 위해 특징의 변별력을 분석하였다. 과도신호 분류는 해양환경 특성상 낮은 신호대잡음비(SNR)를 가지므로 잡음변화에 강인한 특징이 요구된다. 천해 배경잡음을 모델링하기 위해 이론적인 잡음 모델과 Wenz의 천해 관측 자료 그리고 Yule walker 필터를 이용하였다. 과도신호의 SNR에 따른 각 특징의 변별력은 Fisher score를 이용하여 분석하였다. 변별력이 높은 특징을 선택하여 24 클래스의 과도신호원에 대한 분류정확도를 분석한 결과 잡음이 없는 환경에서 선택된 특징에서 상대적으로 높은 분류정확도를 보였다. 이러한 결과를 토대로 최종적으로 선택된 특징은 전체 28가지 특징 중 16가지 특징이 선택되었다. 다중 클래스 SVM분류기를 이용하여 선택된 특징의 인식률 분석결과 과도신호의 SNR 20dB 환경에서 약92%의 분류정확도를 보였다.

Keywords

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