Title: Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features,
Abstract: A current-aided inertial navigation framework is proposed for small
autonomous underwater vehicles in long-duration operations (> 1 hour), where
neither frequent surfacing nor consistent bottom-tracking are available. We
instantiate this concept through mid-depth, underwater navigation. This
strategy mitigates dead-reckoning uncertainty of a traditional inertial
navigation system by comparing the estimate of local, ambient flow velocity
with preloaded ocean current maps. The proposed navigation system is
implemented through a marginalized particle filter where the vehicle's states
are sequentially tracked along with sensor bias and local turbulence that is
not resolved by general flow prediction. The performance of the proposed
approach is first analyzed through Monte Carlo simulations in two artificial
background flow fields, resembling real-world ocean circulation patterns,
superposed with smaller-scale, turbulent components with Kolmogorov energy
spectrum. The current-aided navigation scheme significantly improves the
dead-reckoning performance of the vehicle even when unresolved, small-scale
flow perturbations are present. For a 6-hour navigation with an
automotive-grade inertial navigation system, the current-aided navigation
scheme results in positioning estimates with under 3% uncertainty per distance
traveled (UDT) in a turbulent, double-gyre flow field, and under 7.3% UDT in a
turbulent, meandering jet flow field. Further evaluation with field test data
and actual ocean simulation analysis demonstrates consistent performance for a
6-hour mission, positioning result with under 25% UDT for a 24-hour navigation
when provided direct heading measurements, and terminal positioning estimate
with 16% UDT at the cost of increased uncertainty at an early stage of the
navigation.