Multi-UAV Factor Graph Colocation Algorithm

Abstract

To tackle the difficulty and high cost of UAV positioning in a 5G disaster environment, this study proposes a multi-UAV coordinated positioning algorithm based on a factor graph (MUAV-FGC). A factor graph model is designed for coordinated positioning of the master-slave UAVs. In the positioning process, the influence of the speed and heading errors of the UAV on the positioning accuracy was analyzed, and the expected and variance values of the variables were used as the information transmitted between the factors. The error of the cooperative positioning algorithm was derived based on the factor graph. The coordinated positioning of multiple drones and the positioning accuracy improved. The simulation results show that, compared with the traditional extended Kalman filter (EKF) algorithm, the proposed MUAV-FGC positioning algorithm reduces the mean positioning and root mean square error by approximately 18.86% and 17.54%, respectively, thus proving its effectiveness.