The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Initialization of Cost Function for ML-Based DOA Estimation

ML 알고리즘 기반의 도래각 추정을 위한 비용 함수의 초기화 방법 비교

  • 조상호 (세종대학교 정보통신공학과) ;
  • 이준호 (세종대학교 정보통신공학과)
  • Published : 2008.01.31
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Abstract

Maximum likelihood(ML) diretion-of-arrival(DOA) estimation is essentially optimization of multivariable nonlinear cost function. Since the final estimate is highly dependent on the initial estimate, an initialization is critical in nonlinear optimization. We propose a multi-dimensional(M-D) search scheme of uniform exhaustive search and improved exhaustive search. Improved exhaustive search is superior to uniform exhaustive search in terms of the computational complexity and the accuracy of the estimates.

ML(Maximum Likelihood) 방법 기반의 도래각(Direction-of-Arrival, DOA) 추정은 본질적으로 다차원 비선형 비용 함수의 최적화 과정이다. 초기 추정치에 기울기(gradient) 기반 탐색을 적용하여 최종 추정치를 구하기 때문에, 초기 추청치의 정확성은 매우 중요하다. 본 논문은 균등한 전체탐색(uniform exhaustive search)과 개선된 전체탐색(improved exhaustive search)의 다차원 탐색 방법을 제안한다. 개선된 전체탐색 기법은 균등한 전체탐색의 기법에 비하여 정확성 측면과 계산량 측면에서 우수하다.

Keywords

References

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