To address the problem that unmanned aerial vehicle(UAV) positioning services in urban environments are susceptible to multipath effects and global navigation satellite system(GNSS)signal interruptions

a novel joint fusion architecture is proposed to enhance its robustness.This method integrates GNSS

inertial measurement unit(IMU)

monocular camera and barometer data

employing an extended Kalman filter(EKF)as a local filter and innovatively using a gated recurrent unit(GRU)neural network block as the main filter for information fusion.Visual data

barometer data and GNSS/IMU data are collected using the AirSim virtual environment and Spirent GSS7000 hardware

respectively

and tests are conducted under various lighting and weather conditions

with particular attention focused on multipath and GNSS interruption scenarios.Experimental results show that compared with the traditional EKF joint architecture

the new architecture exhibits better resilience and robustness under various degradation conditions

and the horizontal positioning error maintains an accuracy of 0.55m even in dusty weather scenarios

maintaining a good performance level.