INTEGRATION OF A LOW-COST GLOBAL NAVIGATION SATELLITE SYSTEM TO A SINGLE-BOARD COMPUTER USING KALMAN FILTERING
ABSTRACT The global navigation satellite system (GNSS) is the basis for localized crop management by allowing the georeferencing of collected data and the generation of maps by different systems that compose precision agriculture. There is a demand for low-cost navigation systems to enable their use...
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| Format: | Article |
| Language: | English |
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Sociedade Brasileira de Engenharia Agrícola
2019-06-01
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| Series: | Engenharia Agrícola |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000300323&tlng=en |
| _version_ | 1798022242321301504 |
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| author | Thales M. de A. Silva Grégory de O. Mayrink Domingos S. M. Valente Daniel M. Queiroz |
| author_facet | Thales M. de A. Silva Grégory de O. Mayrink Domingos S. M. Valente Daniel M. Queiroz |
| author_sort | Thales M. de A. Silva |
| collection | DOAJ |
| description | ABSTRACT The global navigation satellite system (GNSS) is the basis for localized crop management by allowing the georeferencing of collected data and the generation of maps by different systems that compose precision agriculture. There is a demand for low-cost navigation systems to enable their use in agriculture. Therefore, the objective of this study is to integrate a low-cost GNSS module to a single-board computer using Kalman filtering to obtain navigation data. The system was evaluated by performing one static and two kinematic experiments, with three repetitions each. In the static experiment, the mean error was 3.25 m with a root mean square error (RMSE) of 3.73 m. In the first kinematic experiment, data variability was lower at a velocity of 1.39 m s−1. In the second kinematic experiment, the mean error was 1.26 and 1.13 m, and the RMSE was 1.45 and 1.27 m for data obtained before and after filtering, respectively. In conclusion, the system reduces the lateral errors in linear sections but is not indicated for sections that change direction. Moreover, this system can be used in agricultural applications such as soil sampling and crop yield monitoring. |
| first_indexed | 2024-04-11T17:26:45Z |
| format | Article |
| id | doaj.art-5aca0ad34dc4408fac92223b5418af1c |
| institution | Directory Open Access Journal |
| issn | 0100-6916 |
| language | English |
| last_indexed | 2024-04-11T17:26:45Z |
| publishDate | 2019-06-01 |
| publisher | Sociedade Brasileira de Engenharia Agrícola |
| record_format | Article |
| series | Engenharia Agrícola |
| spelling | doaj.art-5aca0ad34dc4408fac92223b5418af1c2022-12-22T04:12:18ZengSociedade Brasileira de Engenharia AgrícolaEngenharia Agrícola0100-69162019-06-0139332333010.1590/1809-4430-eng.agric.v39n3p323-330/2019INTEGRATION OF A LOW-COST GLOBAL NAVIGATION SATELLITE SYSTEM TO A SINGLE-BOARD COMPUTER USING KALMAN FILTERINGThales M. de A. Silvahttps://orcid.org/0000-0002-1924-6671Grégory de O. MayrinkDomingos S. M. ValenteDaniel M. QueirozABSTRACT The global navigation satellite system (GNSS) is the basis for localized crop management by allowing the georeferencing of collected data and the generation of maps by different systems that compose precision agriculture. There is a demand for low-cost navigation systems to enable their use in agriculture. Therefore, the objective of this study is to integrate a low-cost GNSS module to a single-board computer using Kalman filtering to obtain navigation data. The system was evaluated by performing one static and two kinematic experiments, with three repetitions each. In the static experiment, the mean error was 3.25 m with a root mean square error (RMSE) of 3.73 m. In the first kinematic experiment, data variability was lower at a velocity of 1.39 m s−1. In the second kinematic experiment, the mean error was 1.26 and 1.13 m, and the RMSE was 1.45 and 1.27 m for data obtained before and after filtering, respectively. In conclusion, the system reduces the lateral errors in linear sections but is not indicated for sections that change direction. Moreover, this system can be used in agricultural applications such as soil sampling and crop yield monitoring.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000300323&tlng=engeoreferencingpositioningbeaglebone blackalgorithm |
| spellingShingle | Thales M. de A. Silva Grégory de O. Mayrink Domingos S. M. Valente Daniel M. Queiroz INTEGRATION OF A LOW-COST GLOBAL NAVIGATION SATELLITE SYSTEM TO A SINGLE-BOARD COMPUTER USING KALMAN FILTERING Engenharia Agrícola georeferencing positioning beaglebone black algorithm |
| title | INTEGRATION OF A LOW-COST GLOBAL NAVIGATION SATELLITE SYSTEM TO A SINGLE-BOARD COMPUTER USING KALMAN FILTERING |
| title_full | INTEGRATION OF A LOW-COST GLOBAL NAVIGATION SATELLITE SYSTEM TO A SINGLE-BOARD COMPUTER USING KALMAN FILTERING |
| title_fullStr | INTEGRATION OF A LOW-COST GLOBAL NAVIGATION SATELLITE SYSTEM TO A SINGLE-BOARD COMPUTER USING KALMAN FILTERING |
| title_full_unstemmed | INTEGRATION OF A LOW-COST GLOBAL NAVIGATION SATELLITE SYSTEM TO A SINGLE-BOARD COMPUTER USING KALMAN FILTERING |
| title_short | INTEGRATION OF A LOW-COST GLOBAL NAVIGATION SATELLITE SYSTEM TO A SINGLE-BOARD COMPUTER USING KALMAN FILTERING |
| title_sort | integration of a low cost global navigation satellite system to a single board computer using kalman filtering |
| topic | georeferencing positioning beaglebone black algorithm |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000300323&tlng=en |
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