Algorithms to improve unmanned aerial vehicle positioning accuracy using European Geostationary Navigation Overlay Service and System for Differential Corrections and Monitoring ionospheric corrections

This paper presents a modified algorithm for determining the positioning accuracy of a unmanned aerial vehicle (UAV) based on a joint Global Positioning System/European Geostationary Navigation Overlay Service + Global Positioning System/System for Differential Corrections and Monitoring (GPS/EGNOS+GPS/SDCM) solution. Firstly, the average weighted model for determining the position of the UAV was developed. The algorithm takes into account the coordinates from the individual GPS/EGNOS and GPS/SDCM solution as well as correction coefficients that are a function of the inverse of the ionospheric vertical TEC (VTEC) delay. Next the accuracy term was estimated in the form of the position errors and root mean square (RMS) errors. Finally the Kalman filter algorithm was used for improved the position errors and RMS errors. The developed algorithm is concerned with determining the positioning accuracy of the UAV for BLh (B-latitude, L-longitude, h-ellipsoidal height) ellipsoidal coordinates. The algorithm was tested on kinematic Global Positioning System/Satellite Based Augmentation System (GPS/SBAS) data recorded by a Global Navigation Satellite System (GNSS) receiver placed on a DJI Matrice 300RTK type unmanned platform. As part of the research test, two flights of the UAV were performed on 16 March 2022 in Olsztyn. In the first flight, the proposed algorithm enabled an increase in UAV positioning accuracy from 4% to 57% after Kalman filter process. In the second flight, on the other hand, UAV positioning accuracy was increased from 6% to 42%. The developed algorithm enabled an increase in UAV positioning accuracy and was successfully tested in two independent flight experiments. Ultimately, further research is planned to modify the algorithm with other correction coefficients.