Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Analysis of target classification performances of active sonar returns depending on parameter values of SVM kernel functions

SVM 커널함수의 파라미터 값에 따른 능동소나 표적신호의 식별 성능 분석

  • Received : 2013.02.06
  • Accepted : 2013.02.26
  • Published : 2013.05.31
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Abstract

Detection and classification of undersea mines in shallow waters using active sonar returns is a difficult task due to complexity of underwater environment. Support vector machine(SVM) is a binary classifier that is well known to provide a global optimum solution. In this paper, classification experiments of sonar returns from mine-like objects and non-mine-like objects are carried out using the SVM, and classification performance is analyzed and presented with discussions depending on parameter values of SVM kernel functions.

수중 천해 환경에서 능동소나의 반향 신호로 기뢰를 탐지 및 식별하는 일은 복잡한 해양 환경의 영향으로 어려운 문제이다. SVM은 패턴인식 문제에서 최적의 해를 제공하는 이진 분류기이다. 본 논문에서는 SVM을 이용하여 능동소나의 반향 데이터로 기뢰와 같은 금속 물체와 바위를 식별하는 실험을 수행하면서, SVM에 사용되는 커널함수의 파라미터 값의 변화에 따른 식별 성능을 분석하고 제시하였다.

Keywords

References

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  1. 컨볼루션 신경망 기반의 능동소나 표적 식별 vol.21, pp.10, 2013, https://doi.org/10.6109/jkiice.2017.21.10.1909
  2. Underwater Moving Target Classification Using Multilayer Processing of Active Sonar System vol.9, pp.21, 2013, https://doi.org/10.3390/app9214617