Yazar "Çekli S." seçeneğine göre listele

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    Adaptive frequency estimation of distorted power system signals using modified extended kalman filter
    (2011) Uzunog`lu C.P.; Çekli S.; Ug`ur M.
    This research presents a method for frequency estimation of distorted power system signals using a modified Extended Kalman Filter (EKF). Distorted signals which have been obtained from measurements may involve noises which may affect the accuracy of frequency measurement in a power system. Thus, the proposed method is employed to eliminate and filter noise and hence to improve the efficiency of frequency estimation. Computer simulations have been carried out for the performance analysis of the proposed method and the comparison of the results of the proposed method with the former Extended Kalman Filter results are presented. The fast tracking performance of the new approach in power system frequency estimation was emphasized.
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    EM-based recursive tracking algorithm for near-field moving sources
    (2007) Çekli S.; Çekli E.; Kabaoglu N.; Çirpan H.A.
    In this paper, we address the problem of joint tracking of the direction of arrival (DOA) and range parameters of moving sources in the near-field of an antenna array with the Expectation-Maximization (EM) based recursive algorithm. The main characteristic of the proposed recursive EM approach is to include computation of the gradient of the log-likelihood and some form of the complete-data Fisher information matrix. The proposed recursive algorithm in this work assumes that the parameters of interest are described by a linear polynomial model. Simulation results of the suggested algorithm are also presented in order to illustrate the performance of the algorithms. © 2007 IEEE.
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    Near field localization of moving sources with rem algorithms
    (2009) Çekli S.; Çekli E.; Kabaoglu N.
    In this paper, we present the problem of joint tracking of the direction of arrival (DOA) and range parameters of moving sources in the near field of an antenna array with two Expectation- Maximization (EM) based recursive algorithms. The main characteristic of the first Recursive EM (REM) approach is to include computation of the gradient of the log-likelihood function and some form of the complete data Fisher information matrix. Compared to first REM approach, the second one utilizes the stochastic approximation of approximate conditional expectation of the complete data sufficient statistics. The proposed recursive algorithms in this work assume that the parameters of interest are described by a linear polynomial model. This paper concludes by presenting the simulation results of the suggested algorithms in order to illustrate the computational effectiveness of the both algorithms.