Particle methods for bayesian multi-object tracking and parameter estimation

Download

index.pdf

Date

2009

Author

Özkan, Emre

Metadata

Show full item record

Item Usage Stats

158
views

65
downloads

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.

Subject Keywords

Electrical engineering., Computer Hardware., Tracking

URI

http://etd.lib.metu.edu.tr/upload/3/12610986/index.pdf
https://hdl.handle.net/11511/19104

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

A new feedback-based contention avoidance algorithm for optical burst switching networks Toku, Hadi Alper; Schmidt, Şenan Ece; Department of Electrical and Electronics Engineering (2008) In this thesis, a feedback-based contention avoidance technique based on weighted Dijkstra algorithm is proposed to address the contention avoidance problem for Optical Burst Switching networks. Optical Burst Switching (OBS) has been proposed as a promising technique to support high-bandwidth, bursty data traffic in the next-generation optical Internet. Nevertheless, there are still some challenging issues that need to be solved to achieve an effective implementation of OBS. Contention problem occurs when t...
Residual based Adaptive Unscented Kalman filter for satellite attitude estimation Söken, Halil Ersin (2012-12-01) Determining the process noise covariance matrix in Kalman filtering applications is a difficult task especially for estimation problems of the high-dimensional states where states like biases or system parameters are included. This study introduces a simplistic residual based adaptation method for the Unscented Kalman Filter (UKF), which is used for small satellite attitude estimation. For a satellite with gyros and magnetometers onboard, the proposed adaptive UKF algorithm estimates the attitude as well as...
Multipath Characteristics of Frequency Diverse Arrays Over a Ground Plane Cetintepe, Cagri; Demir, Şimşek (Institute of Electrical and Electronics Engineers (IEEE), 2014-07-01) This paper presents a theoretical framework for an analytical investigation of multipath characteristics of frequency diverse arrays (FDAs), a task which is attempted for the first time in the open literature. In particular, transmitted field expressions are formulated for an FDA over a perfectly conducting ground plane first in a general analytical form, and these expressions are later simplified under reasonable assumptions. Developed formulation is then applied to a uniform, linear, continuous-wave opera...
Quantitative measure of observability for linear stochastic systems Subasi, Yuksel; Demirekler, Mübeccel (Elsevier BV, 2014-06-01) In this study we define a new observability measure for stochastic systems: the mutual information between the state sequence and the corresponding measurement sequence for a given time horizon. Although the definition is given for a general system representation, the paper focuses on the linear time invariant Gaussian case. Some basic analytical results are derived for this special case. The measure is extended to the observability of a subspace of the state space, specifically an individual state and/or t...
Joint frequency offset and channel estimation Avan, Muhammet; Candan, Çağatay; Department of Electrical and Electronics Engineering (2008) In this thesis study, joint frequency offset and channel estimation methods for single-input single-output (SISO) systems are examined. The performance of maximum likelihood estimate of the parameters are studied for different training sequences. Conventionally training sequences are designed solely for the channel estimation purpose. We present a numerical comparison of different training sequences for the joint estimation problem. The performance comparisons are made in terms of mean square estimation err...

Citation Formats

E. Özkan, “Particle methods for bayesian multi-object tracking and parameter estimation,” Ph.D. - Doctoral Program, Middle East Technical University, 2009.