Abstract:
Underwater gliders are often used to perform in-situ measurements of oceanographic systems. In particular, there have been a number of recent efforts to use their state e...Show MoreMetadata
Abstract:
Underwater gliders are often used to perform in-situ measurements of oceanographic systems. In particular, there have been a number of recent efforts to use their state estimation capabilities in order to infer physical oceanographic process dynamics, such as modeling currents and eddies. Such inference requires that the dead-reckoning systems used on the vehicles, typically hand-tuned Extended Kalman Filters (EKFs), to be tuned consistently, which can be tedious and burdensome, especially across large numbers of trials. In this work, we describe a method to automatically tune state estimation hyper-parameters used in underwater glider EKFs, that does not require ground truth estimates. This automation is achieved by taking advantage of the normalized innovation squared (NIS) metric, which can be used inside of the objective function of various optimization methods. In this paper, we demonstrate its validity in simulated environments and provide initial results for its use on fielded glider data.
Published in: OCEANS 2019 MTS/IEEE SEATTLE
Date of Conference: 27-31 October 2019
Date Added to IEEE Xplore: 20 January 2020
ISBN Information:
Print on Demand(PoD) ISSN: 0197-7385