Learning drag coefficient of ballistic targets using gaussian process modeling

Download

index.pdf

Date

2019

Author

Kumru, Fırat

Metadata

Show full item record

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

Item Usage Stats

173
views

137
downloads

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 is still on the fly. The accuracy of the impact point prediction highly depends on the treatment of the ballistic coefficient in the prediction model. In this thesis, we propose a method to learn the unknown function that describes the relationship between the speed and the ballistic coefficient of the object from the observations. Then, the function is used to predict the impact point of the ballistic object. The unknown function is learned via Gaussian process in the Bayesian framework. The proposed and conventional methods are comparatively studied in a realistic simulation environment. Extensive simulation studies are conducted to characterize the performance of the proposed method and it is shown that the method has a better impact point prediction performance than the conventional ones in terms of the root mean square error.

Subject Keywords

Ballistics., Keywords: Ballistic Target Tracking, Ballistic Coefficient, Gaussian Process.

URI

http://etd.lib.metu.edu.tr/upload/12624506/index.pdf
https://hdl.handle.net/11511/44165

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Negative information fusion for gaussian process based three-dimensional extended target tracking Sür, Cem Gürkan; Özkan, Emre; Department of Electrical and Electronics Engineering (2021-5-10) 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...
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...
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...
MULTI MODE PROJECTILE TRACKING WITH MARGINALIZED PARTICLE FILTER Bilgin, Ozan Ozgun; Demirekler, Mübeccel (2015-10-30) In this study, dynamic models for thrusting and ballistic flight modes of multi mode projectile are obtained and Marginalization method is applied by separation of the linear and nonlinear parts of state space model. In Marginalized Particle Filter (MPF), dimension of the nonlinear system is reduced so that the model can be utilized to obtain better estimates of the state using the same number of particles as that of standard particle filter. The Extended Kalman Filter (EKF), the Particle Filter (PF) and th...
Multi-target tracking using passive doppler measurements Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2013-04-26) In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using Doppler-only measurements in a passive sensor network. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter successfully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.

Citation Formats

F. Kumru, “Learning drag coefficient of ballistic targets using gaussian process modeling,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Electrical and Electronics Engineering., Middle East Technical University, 2019.