Aiming at the problem of inaccurate attitude and position estimation of quadrotor unmanned aerial vehicles (UAVs) in signal interference environments, a multi-sensor data fusion method based on adaptive extended Kalman filter (AEKF) is proposed. This method fuses GPS and IMU data and adjusts the noise covariance matrix in real time to improve the stability and robustness of state estimation. By establishing the UAV dynamics model and sensor observation model, the AEKF algorithm process is derived, and a simulation system is built on the MATLAB platform. Under different GPS signal interference conditions, the estimation errors and convergence speeds of EKF, UKF, and AEKF algorithms are compared. The results show that within the 10-second interference period of GPS loss, the position root mean square error (RMSE) of AEKF is reduced by 29.8% compared to EKF (from 0.57 m to 0.40 m) and by 20% compared to UKF (from 0.50 m to 0.40 m), verifying the advantages of AEKF in anti-interference ability and error convergence. This research provides technical support for the precise positioning and stable control of UAVs in complex low-altitude airspace.