In view of the harsh environment with uncertain external disturbances such as irregular airflow while in high overload and high dynamics motion state during the in-flight alignment of guidance projectile, an adaptive reduced dimension fifth-order cubature Kalman filter based on sequential quadratic programming ( SRD5-CKF) is proposed in this paper, which uses the projection statistics ( PS) principle to detect the innovation vector in the sliding window. If the innovation vector has outlier, it will be reweighted to adjust the measurement noise covariance matrix in real-time. At the same time, the sequential quadratic programming method with fast solving speed and good convergence is used to solve the adaptive factor matrix, so as to realize the optimal estimation of the measurement noise covariance matrix under complex interference. Simulation experiments have shown that the SRD5-CKF proposed in this paper has a faster convergence speed and higher convergence accuracy under complex disturbances such as short-term strong interference and instantaneous impact. The alignment accuracy of the upward misalignment angle reaches 0. 25°, and the alignment accuracy of the eastward and northward misalignment angles reaches 0. 1°, which can meet the requirements of rapid in-flight alignment of guidance projectile.