Integração de sensores GPS via filtro de Kalman

Abstract

Satellite positioning systems in precision agriculture can be used for agricultural monitoring for optimumuse of resources such as agricultural inputs. Data received fromsatellite systems may have positioning errors of up to 10meters. Low-cost receivers have the most positioning errors, however it is possible to use themallied tomathematical concepts and software technologies so that the positioning error decreases. This work aims to develop a systemthat aims to improve the resolutionof thepositions of a lowcost receiver by applying the Kalman filter algorithm. The Kalman filter is an iterative mathematical algorithm that processes data fromnoisy sensors and estimates an optimal state of the system. This work presents a fusion approach ofGPS receiver data via the Kalman filter to estimate coordinate information, velocity and tracking angle of a system. By means of the data sampled in the athletics track of the UTFPR, an analysis was carried out in order to estimate the accuracy of the sensors and the filtered data. The results demonstrate the efficiency of the Kalman filter applied to the fusion data compared to the GPS receiver data, since the filter result reduced the error presented by the system.