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 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.

摘要 全球导航卫星系统（GNSS）是开展作物本地化田间管理的核心基础，其可对采集的数据进行地理坐标标注，并由构成精准农业体系的各类系统生成相关专题地图。当前农业领域对低成本导航系统存在迫切需求，以推动其在农业中的应用。为此，本研究旨在通过卡尔曼滤波技术，将低成本GNSS模块与单板计算机集成，以获取导航数据。本系统通过开展1次静态实验与2次动态实验进行性能评估，每组实验均重复3次。静态实验中，系统的平均误差为3.25米，均方根误差（root mean square error, RMSE）为3.73米。首次动态实验中，当运动速度为1.39米每秒时，数据的波动性更低。第二次动态实验中，滤波前与滤波后获取的数据的平均误差分别为1.26米与1.13米，对应的均方根误差分别为1.45米与1.27米。综上，本系统可降低直线作业路段的侧向误差，但不适用于存在方向变化的作业路段。此外，该系统可应用于土壤采样、作物产量监测等农业生产场景。