Design of Innovative RWALF and MADTF with 5G and IMU Fusion for 3D Positioning in UAV Scenarios

The rapid development of 5G, especially the millimeter-wave (mmWave) for higher throughput, threedimensional positioning is crucial for unmanned aerial vehicle (UAV) motion. This paper proposes high-precision positioning methods based on an adaptive Extended Kalman Filter (EKF), fusing the coordinates measured by 5 G angle of arrival (AOA) positioning and inertial measurement unit (IMU) measurements for single-point positioning. Two fusion strategies are proposed: residual-weighted adaptive loose fusion (RWALF) and Mahalanobis-acceleration dynamic tight fusion (MADTF). The RWALF algorithm fuses the positioning coordinates of 5 G and IMU based on the adaptive adjustment factor obtained from residual weighting, while the MADTF algorithm adjusts the magnitude of the noise matrix during the measurement based on the computation of Mahalanobis distance and the consistency check of IMU acceleration, thereby achieving an adaptive allocation of weights to the 5 G and IMU measurements. Simulation results show that both algorithms exhibit high positioning accuracy and stability in various motion states, with MADTF outperforming RWALF.