In the field of missile aerodynamic parameter identification， traditional extended kalman filter （EKF） algorithms often encounter issues such as high computational complexity， low accuracy， and difficulties in solving the system’s Jacobian matrix. To address these challenges， an online identification method for missile aerodynamic parameters based on singular value decomposition-cubature kalman filter （SVD-CKF） is proposed. Leveraging the cubature point linearization characteristic of CKF， this method avoids the direct solution of the Jacobian matrix， thereby reducing computational complexity. Additionally， by introducing Singular Value Decomposition （SVD） technology， it effectively resolves the issue of potential negative definiteness in the covariance matrix that may arise in traditional CKF algorithms， further enhancing filter stability. Simulation results demonstrate that in the context of online identification of aerodynamic parameters for six-degree-of-freedom tactical missiles， the SVD-CKF algorithm exhibits higher identification accuracy， faster convergence speed， and stronger robustness.