  This paper presents three batch estimation methods that use noisy ground
velocity and heading measurements from a vehicle executing a circular orbit (or
similar large heading change maneuver) to estimate the speed and direction of a
steady, uniform, flow-field. The methods are based on a simple kinematic model
of the vehicle's motion and use curve-fitting or nonlinear least-square
optimization. A Monte Carlo simulation with randomized flow conditions is used
to evaluate the batch estimation methods while varying the measurement noise of
the data and the interval of unique heading traversed during the maneuver. The
methods are also compared using experimental data obtained with a Bluefin-21
unmanned underwater vehicle performing a series of circular orbit maneuvers
over a five hour period in a tide-driven flow.
