Now showing items 1-5 of 5

  • EM based Stochastic maximum likelihood approach for localization of near-field sources in 3-D 

    The goal of this paper is to estimate the locations of unknown sources in 3-D space from the data collected by a 2-D rectangular array. Various studies employing different estimation methods under near-field and far-field assumptions were presented in the past. In most of the previous studies location estimations of sources at the same plane with the antenna array were carried out by using algorithms having constraints for various situations indeed. In this study location estimations of sources ...

  • Near field parameter estimation of moving sources with recursive expectation maximization algorithm 

    Authors:Cekli, Serap; Cekli, Erdinc; Kabaoǧlu, Nihat; Cirpan, Hakan Ali
    Publisher and Date:(IEEE, 2006)
    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 ...

  • Support vector machines based target tracking techniques 

    Authors:Ozer, Sedat; Cirpan, Hakan Ali; Kabaoǧlu, Nihat
    Publisher and Date:(IEEE, 2006)
    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 ...

  • Support vector regression for surveillance purposes 

    This paper addresses the problem of applying powerful statistical pattern classification algorithm based on kernel functions to target tracking on surveillance systems. 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 to use dynamic model together as feature vectors and makes the byperplane and the support vectors follow the changes in these features. The performance of the ...

  • Unconditional maximum likelihood approach for localization of near-field sources in 3-D space 

    Authors:Kabaoǧlu, Nihat; Cirpan, Hakan Ali; Paker, Selçuk
    Publisher and Date:(IEEE, 2004)
    Since maximum likelihood (ML) approaches have better resolution performance than the conventional localization methods in the presence of less number and highly correlated source signal samples and low signal to noise ratios we propose unconditional ML (UML) method for estimating azimuth elevation and range parameters of near-field sources in 3-D space in this paper Besides these superiorities stability asymptotic unbiasedness asymptotic minimum variance properties are motivated the application ...