Sengupta et al 2012.PDF (405.84 kB)
Accounting for location error in Kalman filters: integrating animal borne sensor data into assimilation schemes
journal contribution
posted on 2023-05-19, 08:11 authored by Sengupta, A, Scott FosterScott Foster, Toby Patterson, Bravington, MData 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 OneVolume
7Issue
8Article number
e42093Number
e42093Pagination
1-8ISSN
1932-6203Department/School
Institute for Marine and Antarctic StudiesPublisher
Public Library of SciencePlace of publication
United StatesRights 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