It’s necessary to consider the impact of Doppler Velocity Log (DVL) measurement outliers and its time-varyingnoise characteristic on navigation accuracy in Strap-down Inertial Navigation System (SINS)/DVL integrated navigation. To addressthe problem, a sliding window robust adaptive Kalman filter for SINS/DVL integrated navigation is proposed. Firstly, the systemerror model is established based on psi angle state transformation. The force item of its velocity is replaced by constant gravity term,so that the accuracy of the model is improved by reducing the speed error caused by the force item. Then the innovation sequence isused to verified outliers. If the outliers exist, the sliding window data is used to correct the innovation sequence and adaptive Kalmanfilter algorithm with attenuating factor is utilized for estimate observation noise. The experimental results show that the new errormodel and sliding window robust adaptive Kalman filter can effectively suppression the position error divergence. The position errorcan be controlled in 1.05% in two hours’ SINS/DVL integrated navigation. The navigation position accuracy is enhanced by 39.66%compared to Kalman filter.