RESEARCH ARTICLE
Analysis of Three Different Kalman Filter Implementations for Agricultural Vehicle Positioning
M. Rodríguez1, J. Gómez*, 2
Article Information
Identifiers and Pagination:
Year: 2009Volume: 3
First Page: 13
Last Page: 19
Publisher ID: TOASJ-3-13
DOI: 10.2174/1874331500903010013
Article History:
Received Date: 4/07/2008Revision Received Date: 6/11/2008
Acceptance Date: 17/12/2008
Electronic publication date: 29/1/2009
Collection year: 2009
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Conventional positioning techniques based on GPS receivers are not accurate enough to be used with autonomous guidance systems. High accuracy GPS receivers can be employed, but the cost of the system would be very high. The alternative solution presented in this article is to combine the data provided by different positioning sensors using a Kalman filter. The described procedure also uses an odometric estimation of the mobile position, based on the kinematic model of the agricultural vehicle. Three different implementations of the Kalman filter are described, using different sensor combinations but based on the same vehicle model.