De Freitas, A., Septier, F., Mihaylova, L. orcid.org/0000-0001-5856-2223 et al. (1 more author) (2015) How Can Subsampling Reduce Complexity in Sequential MCMC Methods and Deal with Big Data in Target Tracking? In: Proceedings of the 18th International Conference on Information Fusion. 18th International Conference on Information Fusion, 06-09 Jul 2015, Washington DC, USA. IEEE , pp. 134-141.
Abstract
Target tracking faces the challenge in coping with large volumes of data which requires efficient methods for real time applications. The complexity considered in this paper is when there is a large number of measurements which are required to be processed at each time step. Sequential Markov chain Monte Carlo (MCMC) has been shown to be a promising approach to target tracking in complex environments, especially when dealing with clutter. However, a large number of measurements usually results in large processing requirements. This paper goes beyond the current state-of-the-art and presents a novel Sequential MCMC approach that can overcome this challenge through adaptively subsampling the set of measurements. Instead of using the whole large volume of available data, the proposed algorithm performs a trade off between the number of measurements to be used and the desired accuracy of the estimates to be obtained in the presence of clutter. We show results with large improvements in processing time, more than 40 % with a negligible loss in tracking performance, compared with the solution without subsampling.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 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. |
Keywords: | Sequential Markov Chain Monte Carlo; Tracking; Sunsampling; Big Data |
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) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 TRAX - 607400 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/K021516/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Feb 2018 09:26 |
Last Modified: | 27 Feb 2018 09:28 |
Published Version: | http://ieeexplore.ieee.org/abstract/document/72665... |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127881 |