Kent, JT orcid.org/0000-0002-1861-8349, Bhattacharjee, S orcid.org/0000-0002-0862-9108, Hussein, II et al. (2 more authors) (2018) Filtering When Object Custody is Ambiguous. In: Proceedings of 2018 21st International Conference on Information Fusion (FUSION). 2018 21st International Conference on Information Fusion (FUSION 2018), 10-13 Jul 2018, Cambridge, UK. IEEE , pp. 1317-1322. ISBN 978-0-9964527-6-2
Abstract
Filtering involves predicting the future state of a space object in orbit about the earth given observations (e.g. angles-only or radar measurements) about its current and past states. The task is simplest when the identity of the object is known. A recently developed “Adapted STructural (AST)” coordinate system enables the task to be carried out in a computationally efficient manner. Propagation for a single state (or a small number of sigma points) can be carried out using Keplerian dynamics or using a numerically more expensive propagator to accommodate perturbation effects. In either case, the uncertainty can be represented in AST coordinates as Gaussian to a high level of accuracy. An Unscented Kalman Filter (UKF) has been developed in this situation; in particular, there is no need to use particle filters. However, when object custody is uncertain, i.e. when the latest observation might correspond to two or more objects in a catalog, the filtering task is more complicated. In this case we propose a mixture of Gaussians in AST coordinates to represent the state. The paper will demonstrate the feasibility of this approach.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2018 ISIF. This is an author produced version of a conference paper published in Proceedings of 2018 21st International Conference on Information Fusion (FUSION). |
Keywords: | AST coordinates; Unscented Kalman Filter; mixture modeling |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Funding Information: | Funder Grant number Air Force Research Lab Munitions Directorate FA9550-16-1-0099 |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Jul 2020 12:41 |
Last Modified: | 31 Jul 2020 02:32 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.23919/icif.2018.8455593 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163723 |