The integration of the strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) has become a basic navigation solution for Autonomous Underwater Vehicles (AUVs). However, DVL cannot obtain the velocity relative to the ground when the distance between the AUV and seabed is over the operating range, which occurs often when AUVs are sailing in the middle layer of the ocean. When the DVL velocity relative to the current is used for an integrated filter, the unknown current velocity is coupled with the measured velocity error, which decreases the positioning accuracy. To address this problem, the effect of unknown coupled current velocity is analyzed from the perspective of filter observability, and an integrated SINS/DVL/virtual velocity navigation method is proposed. The virtual velocity based on the velocity variation extracted from the inertial measurement unit and DVL is constructed and used as an aided measurement for the Kalman filter. With the help of virtual velocity, the current velocity can be easily decoupled from measured SINS velocity error. The results of simulation and experiments demonstrated that the proposed method can effectively improve both the convergence speed and accuracy of velocity error compared with the classical method with SINS/DVL integration and, thus, significantly improve the positioning accuracy.