Aram, P. and Freestone, D.R. (2016) Estimation of the mixing kernel and the disturbance covariance in IDE-based spatiotemporal systems. Signal Processing, 121. pp. 46-53. ISSN 0165-1684
Abstract
The integro-difference equation (IDE) is an increasingly popular mathematical model of spatiotemporal processes, such as brain dynamics, weather systems, and disease spread. We present an efficient approach for system identification based on correlation techniques for linear temporal systems that extended to spatiotemporal IDE-based models. The method is derived from the average (over time) spatial correlations of observations to calculate closed-form estimates of the spatial mixing kernel and the disturbance covariance function. Synthetic data are used to demonstrate the performance of the estimation algorithm.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 eLSEVIER. This is an author produced version of a paper subsequently published in Signal Processing. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Dynamic spatiotemporal modeling; Integro-difference equation (IDE); System identification; Correlation |
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: | 10 Jun 2016 08:33 |
Last Modified: | 15 Nov 2016 18:38 |
Published Version: | http://dx.doi.org/10.1016/j.sigpro.2015.10.031 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.sigpro.2015.10.031 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:100737 |