Nihat Kabaoglu

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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(More)
Keywords: Wideband array processing Target tracking Particle filters Sequential Monte Carlo Support vector machines Support vector regression a b s t r a c t In this work, a support vector regression (SVR) based sequential Monte Carlo method is presented to track wideband moving sources using a linear and passive sensor array for a signal model based on(More)
An efficient quiet period management scheme for cognitive radio systems, " IEEE Trans. Abstract—This paper presents a numerical Bayesian approach for the direction-of-arrival (DOA) tracking of multiple targets using a linear and passive sensor array. In this paper, support vector regression (SVR) method is employed, together with particle filters (PFs), to(More)
In this paper, maximum likelihood estimator is proposed for passive localization of narrowband sources in the spherical coordinates (azimuth, elevation, and range). We adapt Expectation/Maximization iterative method to solve the complicated multi-parameter optimization problem appearing on the 3-D localization problem. The proposed algorithm is based on(More)
In this paper the performance of the deterministic maximum likelihood 3-D location estimator for the near-field sources is studied based on the derivation of Cramér-Rao bounds. In the derivation, the source signals and unknown parameters are assumed to be deterministic while the noise is Gaussian. Furthermore , some insights into the achievable performance(More)
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