2026-06-29T21:03:48Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/43732021-11-10T09:38:22Zcom_10259_4268com_10259_5086com_10259_2604col_10259_4269

Gómez Gil, Jaime Ruiz González, Rubén Alonso Garcia, Sergio 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 http://creativecommons.org/licenses/by/3.0/ info:eu-repo/semantics/openAccess 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