• Title/Summary/Keyword: LM(Levenberg-Marquardt) 최적화

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Performance enhancement of underwater acoustic source localization by nonlinear optimization of multiple parameters (다수 정보들의 비선형 최적화에 의한 수중 음원 위치 추정 성능 향상)

  • Yang, In-Sik;Kwon, Taek-Ik;Kang, Tae-Woong;Kim, Ki-Man
    • The Journal of the Acoustical Society of Korea
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    • v.36 no.6
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    • pp.419-424
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    • 2017
  • TDoA (Time Difference-of Arrival) or DoA (Direction-of-Arrival) can be used for source localization. However, the localizing performance is dependent on relative position between source and receivers, receivers' geometric structure, sound speed, and so on. In this paper we propose a source localization method with enhanced performance that combines multiple information. The proposed method uses the time TDoA, DoA and sound speed as variables. LM (Levenberg-Marquardt) method which is one of nonlinear optimizations is applied. The performances of the proposed method was evaluated by simulation. As result of simulation, the proposed method has the lower average localizing error performance than the previous method.

A Data Fitting Technique for Rational Function Models Using the LM Optimization Algorithm (LM 최적화 알고리즘을 이용한 유리함수 모델의 데이터 피팅)

  • Park, Jae-Han;Bae, Ji-Hun;Baeg, Moon-Hong
    • Journal of Institute of Control, Robotics and Systems
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    • v.17 no.8
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    • pp.768-776
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    • 2011
  • This paper considers a data fitting problem for rational function models using the LM (Levenberg-Marquardt) optimization method. Rational function models have various merits on representing a wide range of shapes and modeling complicated structures by polynomials of low degrees in both the numerator and denominator. However, rational functions are nonlinear in the parameter vector, thereby requiring nonlinear optimization methods to solve the fitting problem. In this paper, we propose a data fitting method for rational function models based on the LM algorithm which is renowned as an effective nonlinear optimization technique. Simulations show that the fitting results are robust against the measurement noises and uncertainties. The effectiveness of the proposed method is further demonstrated by the real application to a 3D depth camera calibration problem.