  The performance of navigation algorithms significantly determines the
trajectory tracking accuracy of the guidance, navigation, and control (GNC)
system of an autonomous underwater vehicle (AUV). In closed-loop operation, the
interaction among path planning, control, and navigation plays a crucial role
in the tracking accuracy of the overall GNC system. A Doppler velocity log
(DVL) is often used for AUVs to measure velocity over the ground, positively
affecting the closed-loop tracking error. However, a DVL may not be installed
in miniaturized AUVs due to limited space and energy. In this paper, a
navigation filter for an underactuated miniature AUV (nanoAUV) is considered
that is mainly based on acoustic localization using a novel highly-miniaturized
ultra-short baseline (USBL) system and a depth pressure sensor. The nanoAUV is
being developed for subglacial lake exploration. We compare two unscented
Kalman filters (UKF) with different prediction models - the classical strapdown
inertial navigation systems (SINS) model and a hydrodynamic motion model (HMM).
To enable a fair comparison, filter parameters are auto-tuned with Bayesian
optimization (BO) for open and closed-loop performance, which is novel in AUV
navigation. The results indicate that BO performs similarly to particle swarm
optimization (PSO) regarding sample efficiency for the proposed problem. To
quantify the GNC tracking performance, we use extensive Monte Carlo
simulations. Results suggest that with BO-tuned navigation filter parameters,
the median tracking error is reduced by up to 50% compared to default
parametrization.
