  This paper presents a novel Long Baseline (LBL) position and velocity
navigation filter for underwater vehicles based directly on the sensor
measurements. The solution departs from previous approaches as the range
measurements are explicitly embedded in the filter design, therefore avoiding
inversion algorithms. Moreover, the nonlinear system dynamics are considered to
their full extent and no linearizations are carried out whatsoever. The filter
error dynamics are globally asymptotically stable (GAS) and it is shown, under
simulation environment, that the filter achieves similar performance to the
Extended Kalman Filter (EKF) and outperforms linear position and velocity
filters based on algebraic estimates of the position obtained from the range
measurements.
