The standard UKF algorithm of GNSS / SINS integrated navigation system lacks the ability to adjust the measurement noise variance and system status anomaly adaptively, which affects the filtering accuracy of the integrated navigation system. A robust adaptive UKF algorithm is proposed in order to solve the above problem. Firstly, this algorithm introduces the variational Bayesian estimation principle to estimate the measurement noise variance in real time. Then, an adaptive factor is constructed to reduce the influence of abnormal system state on the navigation solution, based on the predicted residual of the filter. Finally, this algorithm is applied to GNSS / SINS integrated navigation system. The simulation results show that, compared with the standard UKF algorithm and the robust UKF algorithm, the proposed algorithm can improve the position accuracy by 51. 2% and 9. 3% respectively in the whole simulation period when the statistical characteristics of the measurement noise change, and can reduce the influence of abnormal system model disturbance and filter initial value deviation on the navigation solution. The experimental results show that the proposed algorithm has strong adaptability and robustness, and can improve the accuracy of integrated navigation system in complex environment.