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

The Korean Institute of Electromagnetic Engineering and Science (한국전자파학회)
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Target Prioritization for Multi-Function Radar Using Artificial Neural Network Based on Steepest Descent Method

최급 강하법 기반 인공 신경망을 이용한 다기능 레이다 표적 우선순위 할당에 대한 연구

  • Jeong, Nam-Hoon (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology) ;
  • Lee, Seong-Hyeon (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology) ;
  • Kang, Min-Seok (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology) ;
  • Gu, Chang-Woo (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology) ;
  • Kim, Cheol-Ho (Agency for Defense Development) ;
  • Kim, Kyung-Tae (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology)
  • 정남훈 (포항공과대학교 전자전기공학과) ;
  • 이성현 (포항공과대학교 전자전기공학과) ;
  • 강민석 (포항공과대학교 전자전기공학과) ;
  • 구창우 (포항공과대학교 전자전기공학과) ;
  • 김철호 (국방과학연구소) ;
  • 김경태 (포항공과대학교 전자전기공학과)
  • Received : 2017.11.13
  • Accepted : 2017.12.21
  • Published : 2018.01.31
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Abstract

Target prioritization is necessary for a multifunction radar(MFR) to track an important target and manage the resources of the radar platform efficiently. In this paper, we consider an artificial neural network(ANN) model that calculates the priority of the target. Furthermore, we propose a neural network learning algorithm based on the steepest descent method, which is more suitable for target prioritization by combining the conventional gradient descent method. Several simulation results show that the proposed scheme is much more superior to the traditional neural network model from analyzing the training data accuracy and the output priority relevance of the test scenarios.

표적 우선순위 할당은 다수의 표적이 존재하는 전술 환경에서 다기능 레이다(Multifunction Radar: MFR)가 중요한 표적을 추적하고 레이다 자원을 효율적으로 관리하기 위해 필요한 기능이다. 본 논문에서는 레이다에서 수집한 정보로부터 표적에 대한 우선순위를 산출하는 인공 신경망(Artificial Neural Network: ANN) 모델을 구현한다. 더 나아가, 기존의 경사 하강법(gradient descent) 기반 역전파(backpropagation) 알고리즘을 발전시켜 표적 우선순위 할당에 더욱 적합한 최급 강하법(steepest descent) 기반 신경망 학습 알고리즘을 제안한다. 시뮬레이션에서는 훈련 데이터와 신경망의 결과값 사이의 오차와 특정 테스트 시나리오에서 할당된 우선순위의 합리성을 분석하여 제안된 방법의 성능을 확인한다.

Keywords

References

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