  Cellular-connected unmanned aerial vehicles (UAVs) represent a promising
technology for extending the coverage of 5G and 6G networks in a cost-effective
manner. Additionally, Massive multiple-input multiple-output (MIMO) serves as
an effective solution to interference mitigation in cellular-connected UAV
communications. In this letter, we propose a fusion of wireless and sensor data
to enhance beam alignment for cellular-connected UAV massive MIMO
communications. We develop a predictive beamforming framework, including the
frame structure and predictive beamformer. Moreover, we employ an extended
Kalman filter (EKF) to integrate channel and sensor data and provide the
corresponding state-space and observation models. Simulation results
demonstrate that the proposed scheme can improve position/orientation
estimation accuracy significantly, leading to higher spectral efficiency.
