This paper presents a novel approach to the design of globally asymptotically stable (GAS) position and velocity filters for Autonomous Underwater Vehicles (AUVs) based directly on the sensor readings of an Ultra-short Baseline (USBL) acoustic array system and a Doppler Velocity Log (DVL). The proposed methodology is based on an equivalent linear time-varying (LW) system that fully captures the dynamics of the nonlinear system, allowing for the use of powerful linear system analysis and filtering design tools that yield GAS filter error dynamics. Numerical results using Monte Carlo simulations and comparison to the Bayesian Cramer Rao Bound (BCRB) reveal that the performance of the proposed filter is tight to this theoretical estimation error lower bound. In comparison with other approaches, the present solution achieves the same level of performance of the Extended Kalman Filter (EKF), which does not offer GAS guarantees, and outperforms other classical filtering approaches designed in inertial coordinates instead of the body-fixed coordinate frame. (C) 2011 Elsevier Ltd. All rights reserved.