RESEARCH ARTICLE


Analysis of Three Different Kalman Filter Implementations for Agricultural Vehicle Positioning



M. Rodríguez1, J. Gómez*, 2
1 Lear Corporation, Valls, Tarragona, Spain
2 Departamento de Teoría de la Señal, Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, 47011 Valladolid, Spain


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Creative Commons License
© 2009 Rodríguez and Gómez

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.

* Address correspondence to this author at the Departamento de Teoría de la Señal, Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, 47011 Valladolid, Spain; Tel: (+34) 983423660; E-mail: jgomez@tel.uva.es


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.

Keywords: Automatic guidance, GPS, INS, Kalman filter, Sensor fusion.