Negative information fusion for gaussian process based three-dimensional extended target tracking

Download

CGSUR_Master_Thesis.pdf

Date

2021-5-10

Author

Sür, Cem Gürkan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

420
views

42
downloads

Extended target tracking refers to the estimation of extent of a target as well as its position, kinematics, and orientation. In this thesis, we compare performances of Gaussian process based extended target tracking methods. Additionally, we propose a method that uses negative information fusion in three-dimensional point cloud data to enhance extent estimates of the target. Wide-ranging simulations are carried out to demonstrate the performance of the proposed algorithm. All simulations are carried out on a modular environment to be able to easily integrate different scenarios and compare the performances of the different methods. Results are obtained with both simulated and real data to attain better performance comparisons.

Subject Keywords

Extended target tracking, Gaussian processes, Negative information fusion

URI

https://hdl.handle.net/11511/90809

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Random Matrix Based Extended Target Tracking with Orientation: A New Model and Inference Tuncer, Barkın; Özkan, Emre (2021-02-01) In this study, we propose a novel extended target tracking algorithm which is capable of representing the extent of dynamic objects as an ellipsoid with a time-varying orientation angle. A diagonal positive semi-definite matrix is defined to model objects' extent within the random matrix framework where the diagonal elements have inverse-Gamma priors. The resulting measurement equation is non-linear in the state variables, and it is not possible to find a closed-form analytical expression for the true poste...
Learning drag coefficient of ballistic targets using gaussian process modeling Kumru, Fırat; Özkan, Emre; Department of Electrical and Electronics Engineering (2019) Ballistic object tracking involves estimating an unknown ballistic coefficient which directly affects the dynamics of the object. In most studies, the ballistic coefficient is assumed to be constant throughout the object’s flight. In reality, the ballistic coefficient is a function of the speed of the object and depends on the object’s aerodynamic properties. In the literature, the impact point prediction is defined as predicting the position that the object is expected to hit on the ground while the object...
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 random matrix. The proposed model requires solving two challenging problems. First, the data association problem between the measurements and the sub-objects. Second, the inference problem that involves non-conjugate priors and likelihoods which needs to be solved within the recursive fil...
Posterior Cram'er-Rao Lower Bounds for Extended Target Tracking with Random Matrices Sarıtaş, Elif; Orguner, Umut (2016-07-08) This paper presents posterior Cram'er-Rao lower bounds (PCRLB) for extended target tracking (ETT) when the extent states of the targets are represented with random matrices. PCRLB recursions are derived for kinematic and extent states taking complicated expectations involving Wishart and inverse Wishart distributions. For some analytically intractable expectations, Monte Carlo integration is used. The bounds for the semi-major and minor axes of the extent ellipsoid are obtained as well as those for the exte...
3D Extended Object Tracking Using Recursive Gaussian Processes Kumru, Murat; Özkan, Emre (2018-07-10) In this study, we consider the challenging task of tracking dynamic 3D objects with unknown shapes by using sparse point cloud measurements gathered from the surface of the objects. We propose a Gaussian process based algorithm that is capable of tracking the dynamic behavior of the object and learn its shape in 3D simultaneously. Our solution does not require any parametric model assumption for the unknown shape. The shape of the objects is learned online via a Gaussian process. The proposed method can joi...

Citation Formats

C. G. Sür, “Negative information fusion for gaussian process based three-dimensional extended target tracking,” M.S. - Master of Science, Middle East Technical University, 2021.