Towards Auto-tuning of Kalman Filters for Underwater Gliders based on Consistency Metrics

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.