Coaxial drones have garnered popularity owing to their energy efficiency and compact design. However, the precise navigation of these drones in complex and dynamic flight scenarios is limited by inaccuracies in heading/yaw estimation. Conventional heading estimation methods rely on magnetometers and real-time kinematic Global Navigation Satellite Systems (RTK-GNSS), which directly measure heading angle. However, the small size of microdrones restricts the placement of magnetometers away from magnetic interference and prevents the use of directional antennas. Moreover, single-antenna alignment algorithms are highly susceptible to errors caused by nonlinearity, leading to significant inaccuracies in heading estimation. To address these challenges, this paper proposes a hybrid heading estimation approach that integrates Motion-Adaptive Stabilization with an Angle-Parameterized Extended Kalman Filter (APEKF). This method utilizes low-cost GNSS, a magnetometer, and an Inertial Measurement Unit (IMU). Heading is initialized based on the drone’s static attitude, with an adaptive threshold established during takeoff to account for varying flight conditions. As the drone reaches higher altitudes, heading estimation is further stabilized. GNSS velocity observations enhance estimation accuracy through horizontal maneuvering alignment achieved by incorporating multiple sub-filter techniques and residual-based fusion. In the simulations and onboard experiments in this study, the proposed heading estimation method demonstrated a precision of approximately 1.01° post-takeoff, with the alignment speed enhanced by 43%. Moreover, the method outperformed existing estimation techniques and, owing to its low computational overhead, can serve as a reliable full-stage backup across various drone applications.