The airflow angle is a crucial parameter in flight control and navigation processes. However, accurate measurement and estimation of the airflow angle typically require high-precision sensor systems. To address this issue, this paper proposes an airflow angle estimation algorithm under low-cost sensors. The algorithm is based on the Unscented Kalman Filter (UKF) method, which considers the effect of wind during the derivation of the UKF system model. It also addresses the problem of velocity integration divergence caused by constant biases in low-cost inertial sensors and Attitude and Heading Reference Systems (AHRS). To facilitate algorithm implementation, the filtering model adopts the most compact form. The paper establishes a sensor model and conducts simulation verification. The results show that the airflow angle estimation algorithm based on UKF can integrate output information from multiple sensors, effectively improving the accuracy of airflow angle output in low-cost sensor systems. Moreover, the algorithm is applicable to flight environments with wind disturbances.