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

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posted on 2023-05-19, 08:11 authored by Sengupta, A, Scott FosterScott Foster, Toby Patterson, Bravington, M
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.

History

Publication title

PLoS One

Volume

7

Issue

8

Article number

e42093

Number

e42093

Pagination

1-8

ISSN

1932-6203

Department/School

Institute for Marine and Antarctic Studies

Publisher

Public Library of Science

Place of publication

United States

Rights 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/

Repository Status

  • Open

Socio-economic Objectives

Expanding knowledge in the mathematical sciences

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