  The underactuated vehicles are apposite for the long-term deployment and data
collection in spatiotemporally varying marine environments. However, these
vehicles need to estimate their positions (states) with intrinsic sensing in
their long-term trajectories. In previous studies, autonomous underwater
vehicles have commonly used vision and range sensors for autonomous state
estimation. Inspired by the intrinsic sensing and the persistent deployment, we
investigate the localization problem (state estimation) for an inexpensive and
underactuated drifting vehicle called a drifter. In this paper, we present a
localization method for the drifter making use of the observations of a
proprioceptive sensor, i.e., compass. We create the water flow pattern within a
given region from ocean model predictions, develop a stochastic motion model,
and analyze the persistent water flow behavior. Given a distribution of initial
deployment states of the drifter at a particular depth of the water column
within the region and the water flow pattern, our method finds attractors and
their transient groups at the given depth as the persistent behavior of the
water flow. A most-likely localized trajectory of the drifter for a sequence of
compass observations is generated based on the persistent behavior of the water
flow and hidden Markov model. Our simulation results based on data from ocean
model predictions substantiate good performance of our proposed localization
method with a low error rate of the state estimation in the long-term
trajectory of the drifter.
