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Accounting for location error in Kalman filters: integrating animal borne sensor data into assimilation schemes

Citation

Sengupta, A and Foster, SD and Patterson, TA and Bravington, M, Accounting for location error in Kalman filters: integrating animal borne sensor data into assimilation schemes, PLoS One, 7, (8) Article e42093. ISSN 1932-6203 (2012) [Refereed Article]


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Copyright Statement

Copyright: 2012 Sengupta et al. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1371/journal.pone.0042093

Abstract

Data assimilation is a crucial aspect of modern oceanography. It allows the future forecasting and backward smoothing of ocean state from the noisy observations. Statistical methods are employed to perform these tasks and are often based on or related to the Kalman filter. Typically Kalman filters assumes that the locations associated with observations are known with certainty. This is reasonable for typical oceanographic measurement methods. Recently, however an alternative and abundant source of data comes from the deployment of ocean sensors on marine animals. This source of data has some attractive properties: unlike traditional oceanographic collection platforms, it is relatively cheap to collect, plentiful, has multiple scientific uses and users, and samples areas of the ocean that are often difficult of costly to sample. However, inherent uncertainty in the location of the observations is a barrier to full utilisation of animal-borne sensor data in data assimilation schemes. In this article we examine this issue and suggest a simple approximation to explicitly incorporate the location uncertainty, while staying in the scope of Kalman-filter-like methods. The approximation stems from a Taylor-series approximation to elements of the updating equation.

Item Details

Item Type:Refereed Article
Keywords:Kalman filter, location error, ocean models
Research Division:Mathematical Sciences
Research Group:Statistics
Research Field:Statistics not elsewhere classified
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the mathematical sciences
UTAS Author:Foster, SD (Dr Scott Foster)
UTAS Author:Patterson, TA (Dr Toby Patterson)
UTAS Author:Bravington, M (Dr Mark Bravington)
ID Code:118987
Year Published:2012
Web of Science® Times Cited:3
Deposited By:Ecology and Biodiversity
Deposited On:2017-07-25
Last Modified:2017-08-17
Downloads:101 View Download Statistics

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