Particle methods for bayesian multi-object tracking and parameter estimation

Özkan, Emre
In this thesis a number of improvements have been established for specific methods which utilize sequential Monte Carlo (SMC), aka. Particle filtering (PF) techniques. The first problem is the Bayesian multi-target tracking (MTT) problem for which we propose the use of non-parametric Bayesian models that are based on time varying extension of Dirichlet process (DP) models. The second problem studied in this thesis is an important application area for the proposed DP based MTT method; the tracking of vocal tract resonance frequencies of the speech signals. Lastly, we investigate SMC based parameter estimation problem of nonlinear non-Gaussian state space models in which we provide a performance improvement for the path density based methods by utilizing regularization techniques.


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In this study, the performance and implementation of magneto-acousto-electrical tomography (MAET) is investigated using a linear phased array (LPA) transducer. The goal of MAET is to image the conductivity distribution in biological bodies. It uses the interaction between ultrasound and a static magnetic field to generate velocity current density distribution inside the body. The resultant voltage due to velocity current density is sensed by surface electrodes attached on the body. In this study, the theory...
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Improvements in DOA estimation by array interpolation in non-uniform linear arrays
Yaşar, Temel Kaya; Tuncer, Temel Engin; Department of Electrical and Electronics Engineering (2006)
In this thesis a new approach is proposed for non-uniform linear arrays (NLA) which employs conventional subspace methods to improve the direction of arrival (DOA) estimation performance. Uniform linear arrays (ULA) are composed of evenly spaced sensor elements located on a straight line. ULA's covariance matrix have a Vandermonde matrix structure, which is required by fast subspace DOA estimation algorithms. NLA differ from ULA only by some missing sensor elements. These missing elements cause some gaps in...
Citation Formats
E. Özkan, “Particle methods for bayesian multi-object tracking and parameter estimation,” Ph.D. - Doctoral Program, Middle East Technical University, 2009.