This paper proposes a SUKF-KF hybrid filtering algorithm suitable for initial alignment of SINS static base with large azimuth misalignment angles,in response to the problem of high computational complexity and low computational efficiency of the initial alignment UKF algorithm.The nonlinear error model is decomposed into linear and nonlinear parts,and KF filtering is used to process the linear part.Since the measurement equation of the nonlinear part is linear,the updated part of the measurement can be linearized and processed by using the simplified unscented Kalman filtering method.The simulation results show that:1) The convergence speed of the SUKF-KF algorithm is increased by 14% and 16% respectively compared with the UKF algorithm under two large azimuth misalignment angles,and the convergence accuracy is comparable to that of the UKF;and 2) The SUKF-KF algorithm can effectively reduce computational complexity and improve real-time performance.