An Implementation of Accuracy Improvement for Low-Cost GPS Tracking Using Kalman Filter with Raspberry Pi

GPS is widely extended to various scenarios, owing to its trust and utilities. The GPS on smartphones is more accurate than the GPS receivers. On average, modern smartphones are already using A-GPS to help the global navigation system quickly lock-in smartphone satellites. Although the GPS receiver is used for renewable technologies, such as automated drone applications for farming that require high accuracy, there is a clear need for large-scale precision farm monitoring to increase productivity to meet rising population demands. Drones need accurate GPS to map the agricultural land and precisely spray the land GPS Coordinate data from GPS receiver has often been due to inaccuracies caused by many different factors that GPS signals have made. The main cause of error affecting the accuracy and precision of GPS positioning is the lack of the number of satellites in the sky. We paid attention to the GPS calculation method itself. We adapted the Kalman filter to calculate the positioning of the GPS. Kalman filter is used by direct GPS measurements to make better location estimates than those given. The Kalman filter parameter has been adapted to improve the accuracy and accuracy of SPS positioning in stand-alone mode in worse situations. The error can, therefore, be reduced by integrating the Kalman filter as a post-processing filter. The test result shows that Kalman Filter produces a position that is more accurate than the covariance noise value parameter (R) = 10–6 and the covariance noise (Q) = 10–6.