Teknik Bilimler Meslek Yüksekokulu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/2671

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    Article
    Citation Count: 1
    Support vector regression for surveillance purposes
    (Springer-Verlag Berlin, 2006) Özer, Sedat; Çırpan, Hakan Ali; Kabaoğlu, Nihat
    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 tracker is demonstrated in a sensor network scenario with a constant velocity moving target on a plane for surveillance purpose.
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    Conference Object
    Citation Count: 9
    Unconditional maximum likelihood approach for localization of near-field sources in 3-D space
    (IEEE, 2004) Kabaoğlu, Nihat; Çırpan, Hakan Ali; Paker, Selçuk
    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 of ML approach. Despite these advantages ML estimator has computational complexity. Fortunately this problem can be tackled by the application of Expectation/Maximization (EM) iterative algorithm which converts the multidimensional search problem to one dimensional parallel search problems in order to prevent computational complexity.