Gaussian Processes and Bayesian Inference

In recent years, several methods have been proposed to combine multiple kernels instead of using a single one. These different kernels may correspond to using different notions of similarity or may be using information coming from multiple sources (different representations or different feature subsets). In trying to…

Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular importance in identification of nonlinear dynamic systems from experimental data. 1) It allows us to combine derivative infor…

In extended target tracking, targets potentially produce more than one measurement per time step. Multiple extended targets are therefore usually hard to track, due to the resulting complex data association. The main contribution of this paper is the implementation of a Probability Hypothesis Density (PHD) filter for…

This correspondence proposes a new measurement update for extended target tracking under measurement noise when the target extent is modeled by random matrices. Compared to the previous measurement update developed by Feldmann , this work follows a more rigorous path to derive an approximate measurement update using t…

• UMUT ORGUNER

We present an adaptive smoother for linear state-space models with unknown process and measurement noise covariances. The proposed method utilizes the variational Bayes technique to perform approximate inference. The resulting smoother is computationally efficient, easy to implement, and can be applied to high dimensi…

• UMUT ORGUNER

This paper presents a new prediction update for extended targets whose extensions are modeled as random matrices. The prediction is based on several minimizations of the Kullback-Leibler divergence (KL-div) and allows for a kinematic state dependent transformation of the target extension. The results show that the ext…

• UMUT ORGUNER

In Gilholm et al.'s extended target model, the number of measurements generated by a target is Poisson distributed with measurement rate γ. Practical use of this extended target model in multiple extended target tracking algorithms requires a good estimate of γ. In this paper, we first give a Bayesian recursion for es…

The use of a sigma-point approximation is proposed for the Chernoff fusion of Gaussian mixtures. The superiority of the proposed method to its best alternatives in the literature is shown on both a simple density fusion problem and a distributed multiple target tracking example, where the outputs of Gaussian mixture c…

Özet henüz yok.

The world in which we live is becoming more and more automated, exemplified by the numerous robots, or autonomous vehicles, that operate in air, on land, or in water. These robots perform a wide array of different tasks, ranging from the dangerous, such as underground mining, to the boring, such as vacuum cleaning. In…

Extended target tracking (ETT) is an issue in high-resolution radar surveillance, ship tracking, and video tracking. Most of the previous works focus on tracking an ellipsoidal extended target without measurement origin uncertainty (missed detections and clutter). In this paper, a new estimator called the Gaussian pro…

• MURAT EFE

Object superposition is a way to derive Bayesian estimators for multiple object tracking using point processes. A low computational complexity Bayesian multiple target tracking filter, based on target superposition, is presented. The concept of superposition is introduced and applied to the well-known Joint Probabilis…

• MURAT EFE