The Journal of Korean Institute of Electromagnetic Engineering and Science (한국전자파학회논문지)

The Korean Institute of Electromagnetic Engineering and Science (한국전자파학회)
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A Study on Shell-Shaped Target Classification Using RCS and Fuzzy Classifier

RCS와 퍼지 구분기를 이용한 포탄 형태의 표적 식별기법에 대한 연구

  • Lee, Seung-Jae (Department of Electrical Engineering, Pohang University of Science and Technology) ;
  • Jung, Sung-Jae (Department of Electrical Engineering, Pohang University of Science and Technology) ;
  • Kang, Byung-Soo (Department of Electrical Engineering, Pohang University of Science and Technology) ;
  • Na, Hyung-Gi (LIGNEX1) ;
  • Kim, Hyun (LIGNEX1) ;
  • Kim, Kyung-Tae (Department of Electrical Engineering, Pohang University of Science and Technology)
  • Received : 2013.12.12
  • Accepted : 2014.02.10
  • Published : 2014.05.31
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Abstract

In this paper, a study on the optimization of fuzzy classifier using radar cross section(RCS) values is presented to classify shell-shaped targets. Method of moments(MOM) is exploited to construct RCS database of generic shell-shaped targets in uniform angular intervals. Relative orientations are estimated from various flight scenarios of shell-shaped targets, and associated RCS values are interpolated from the generated RCS database with uniform angular intervals. Initial membership functions are determined using the interpolated RCS values, and particle swarm optimization(PSO) is utilized to optimize the membership functions of the fuzzy classifier in terms of probability of correct classification.

본 논문에서는 포탄 형태의 표적을 식별하기 위해 레이더 단면적(radar cross section: RCS)을 이용하여 퍼지 구분기를 최적화하는 연구를 제시한다. 일반적인 포탄 형태의 표적들에 대한 RCS 데이터베이스를 일정 각도 간격으로 획득하기 위해 모멘트법(method of moments: MOM)을 이용한다. 상대적인 자세각들을 포탄들의 다양한 비행 시나리오로부터 추정하고, 그 각도들에 연관된 RCS 값들을 일정한 각도 간격으로 만들어진 RCS 데이터베이스로부터 보간한다. 그 보간된 RCS 값들로부터 최초 멤버쉽 함수들을 결정하고, particle swarm optimization(PSO)을 이용하여 퍼지 구분기의 멤버쉽 함수들을 식별 확률의 관점에서 최적화 한다.

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