Umut Orguner

E-mail

umut@metu.edu.tr

Department

Department of Electrical and Electronics Engineering

ORCID

0000-0002-7670-5635

Scopus Author ID

6506245711

Web of Science Researcher ID

A-5234-2013

On Riemannian Angle Tracking of a Nearly Constant Velocity Target on the Unit Sphere Orguner, Umut; Ulgen, Asli Gunduz; Kosun, Gizem Ortac (2025-01-01) This paper presents a new state-space model and tracking filter for Riemannian angle tracking on the unit sphere. We consider a scenario where noisy angle-only measurements (azimuth and elevation) of a target, moving with ...
Bayesian Filtering with Unknown Process Noise Covariance Laz, Eray; Orguner, Umut (2025-01-01) Bayesian filtering problem is considered in linear Gaussian systems with unknown inverse Wishart distributed process noise covariance. A Bayesian filter is formulated to approximate the joint posterior for the state and th...
MSE of Kalman Filter and Smoother for Fixed Non-Random State Trajectories Kurt, Batin; Orguner, Umut (2025-01-01) We present analytical expressions for predicting the mean square error (MSE) performance of the Kalman filter (KF) and Kalman smoother (KS) when applied to fixed, non-random state trajectories. Unlike the standard Bayesian...
Adaptive mixture approximation for target tracking in clutter D'Ortenzio, Alessandro; Manes, Costanzo; Orguner, Umut (2023-11-22) Target tracking is a state estimation problem common in many practical scenarios like air traffic control, autonomous vehicles, marine radar surveillance and so on. In a Bayesian perspective, when phenomena like clutter ar...
Gaussian Mixture Filtering with Nonlinear Measurements Minimizing Forward Kullback-Leibler Divergence Laz, Eray; Orguner, Umut (2023-07-01) A Gaussian mixture filter is proposed for the state estimation of dynamical systems with nonlinear measurements. The filter is derived by solving an assumed density filtering problem where Kullback-Leibler (KL) divergence ...
An Approximate MSE Expression for Maximum Likelihood and Other Implicitly Defined Estimators of Non-Random Parameters Mehmetcik, Erdal; Orguner, Umut; Candan, Çağatay (2023-03-01) An approximate mean square error (MSE) expression for the performance analysis of implicitly defined estimators of non-random parameters is proposed. An implicitly defined estimator (IDE) declares the minimizer/maximizer o...
Expectation Propagation with Context Adjustment for Smoothing of Jump Markov Linear Systems Sartas, Elif; Orguner, Umut (2023-01-01) A fixed interval smoother for jump Markov linear systems (JMLSs) is proposed in the framework of expectation propagation (EP). The concept of context adjustment is introduced into EP to avoid/alleviate indefinite covarianc...
Adaptive Mixture Model Reduction based on the Composite Transportation Dissimilarity D'Ortenzio, Alessandro; Manes, Costanzo; Iuliis, Vittorio De; Orguner, Umut (2023-01-01) Providing efficient yet accurate statistical models is a challenging problem in many applications. When elementary models are not sufficiently descriptive, mixtures of densities can be used. A complexity management issue a...
Bayesian Filtering and Smoothing with Unknown Measurement Noise Covariance Laz, Eray; Orguner, Umut (2023-01-01) Bayesian filtering and smoothing problems with unknown measurement noise covariance are investigated for linear Gaussian systems. The measurement noise covariance is assumed to be inverse Wishart distributed. A Bayesian fi...
Multi-Ellipsoidal Extended Target Tracking With Variational Bayes Inference Tuncer, Barkın; Orguner, Umut; Özkan, Emre (2022-01-01) In this work, we propose a novel extended target tracking algorithm, which is capable of representing a target or a group of targets with multiple ellipses. Each ellipse is modeled by an unknown symmetric positive-definite...

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