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Browsing Yüksekokullar by Author "Cirpan, Hakan Ali"

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    Near field parameter estimation of moving sources with recursive expectation maximization algorithm
    (IEEE, 2006) Cekli, Serap; Cekli, Erdinc; Kabaoğlu, Nihat; Cirpan, Hakan Ali
    In this paper maximum likelihood (ML) estimator is proposed for the joint estimation of the direction of arrival (DOA) and range parameters of moving sources in the near-field of the antenna array. ML estimation algorithm is presented for deterministic signal model. Recursive form of the expectation maximization (REM) algoritm is suggested for the estimation of the near-field parameters because there is not closed form solutions for the maximum likelihood functions. Moreover simulation results of the suggested algorithm are presented.
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    Near field parameter estimation of moving sources with recursive expectation maximization algorithm [Yinelemeli beklenti/en büyükleme algoritması ile hareketli kaynakların yakın-alan parametrelerinin kestirimi]
    (2006) Çekli, Serap; Çekli, Erdinç; Kabaoğlu, Nihat; Cirpan, Hakan Ali
    In this paper maximum likelihood (ML) estimator is proposed for the joint estimation of the direction of arrival (DOA) and range parameters of moving sources in the near-field of the antenna array. ML estimation algorithm is presented for deterministic signal model. Recursive form of the expectation maximization (REM) algoritm is suggested for the estimation of the near-field parameters because there is not closed form solutions for the maximum likelihood functions. Moreover simulation results of the suggested algorithm are presented. © 2006 IEEE.
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    Citation Count: 1
    Support vector machines based target tracking techniques [Destek vektör makineleri tabanlı hedef takip yöntemleri]
    (2006) Özer, Sedat; Cirpan, Hakan Ali; Kabaoğlu, Nihat
    This paper addresses the problem of aplying powerful statistical pattern classification algorithms based on kernels to target tracking. Rather than directly adapting a recognizer we develop a localizer directly using the regression form of the Support Vector Machines (SVM). The proposed approach considers using dynamic model together as feature vectors and makes the hyperplane and the support vectors follow the changes in these features. The performance of the tracker is demostrated in a sensor network scenario with a moving target in a polynomial route. © 2006 IEEE.