2026-04-21T10:53:10Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/43732021-11-10T09:38:22Zcom_10259_4268com_10259_5086com_10259_2604col_10259_4269
Repositorio Institucional de la Universidad de Burgos
author
Gómez Gil, Jaime
author
Ruiz González, Rubén
author
Alonso Garcia, Sergio
author
Gómez Gil, Francisco Javier
2017-03-17T08:36:52Z
2017-03-17T08:36:52Z
2013-11
1424-8220
http://hdl.handle.net/10259/4373
10.3390/s131115307
Low-cost GPS receivers provide geodetic positioning information using the
NMEA protocol, usually with eight digits for latitude and nine digits for longitude. When
these geodetic coordinates are converted into Cartesian coordinates, the positions fit in a
quantization grid of some decimeters in size, the dimensions of which vary depending on
the point of the terrestrial surface. The aim of this study is to reduce the quantization errors
of some low-cost GPS receivers by using a Kalman filter. Kinematic tractor model
equations were employed to particularize the filter, which was tuned by applying Monte
Carlo techniques to eighteen straight trajectories, to select the covariance matrices that
produced the lowest Root Mean Square Error in these trajectories. Filter performance was
tested by using straight tractor paths, which were either simulated or real trajectories
acquired by a GPS receiver. The results show that the filter can reduce the quantization
error in distance by around 43%. Moreover, it reduces the standard deviation of the heading
by 75%. Data suggest that the proposed filter can satisfactorily preprocess the low-cost GPS
receiver data when used in an assistance guidance GPS system for tractors. It could also be
useful to smooth tractor GPS trajectories that are sharpened when the tractor moves over
rough terrain.
eng
Attribution 3.0 Unported
Kalman filter
agricultural vehicle
Global Positioning System (GPS)
vehicle guidance
sensor data fusion
autonomous navigation
A Kalman Filter Implementation for Precision Improvement in Low-Cost GPS Positioning of Tractors
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/4373/1/Gomez-Sensors_2013.pdf
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https://riubu.ubu.es/bitstream/10259/4373/6/Gomez-Sensors_2013.pdf.txt
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