Li, X., Yang, L., Mihaylova, L.S. orcid.org/0000-0001-5856-2223 et al. (2 more authors) (2018) Enhanced GMM-based Filtering with Measurement Update Ordering and Innovation-based Pruning. In: Proceedings of the International Conference on Information Fusion. International Conference on Information Fusion, 13 Jul 2018 - 13 Jul 2017, Cambridge, UK. IEEE ISBN 978-0-9964527-6-2
Abstract
The Gaussian mixture model (GMM) has been extensively investigated in nonlinear/non-Gaussian filtering problems. This paper presents two enhancements for GMM-based nonlinear filtering techniques, namely, the adaptive ordering of the measurement update and normalized innovation square (NIS)-based mixture component management. The first technique selects the order of measurement update by maximizing the marginal measurement likelihood to improve performance. The second approach takes the filtering history of a mixture component into account and prunes those components with NIS larger than a threshold to eliminate their impact on the filtering posterior. The advantage of the proposed enhancements is illustrated via simulations that consider source tracking using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements received at two unmanned aerial vehicles (UAVs). A GMM-cubature quadrature Kalman filter (CQKF) is implemented and its performances with different measurement update and mixture component management strategies are compared. The superior performance obtained via the use of the two proposed techniques is demonstrated.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Jun 2018 10:19 |
Last Modified: | 19 Dec 2022 13:49 |
Published Version: | https://doi.org/10.23919/ICIF.2018.8455666 |
Status: | Published online |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/ICIF.2018.8455666 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:131394 |