Theoretical Advances in Current Estimation and Navigation from a Glider-Based Acoustic Doppler Current Profiler (ADCP)

TL;DR

This paper develops a joint probabilistic model for estimating glider position, velocity, and subsurface currents from ADCP data, correcting previous assumptions and extending Kalman smoothing, with simulations confirming improved accuracy and uncertainty quantification.

Contribution

It introduces a corrected joint probability model for ADCP-based navigation, extending Kalman smoothing to improve estimation accuracy and uncertainty quantification.

Findings

Simulations demonstrate improved estimation accuracy.

The joint model corrects previous independence assumptions.

Framework sets stage for incorporating additional data and constraints.

Abstract

We examine acoustic Doppler current profiler (ADCP) measurements from underwater gliders to determine glider position, glider velocity, and subsurface current. ADCPs, however, do not directly observe the quantities of interest; instead, they measure the relative motion of the vehicle and the water column. We examine the lineage of mathematical innovations that have previously been applied to this problem, discovering an unstated but incorrect assumption of independence. We reframe a recent method to form a joint probability model of current and vehicle navigation, which allows us to correct this assumption and extend the classic Kalman smoothing method. Detailed simulations affirm the efficacy of our approach for computing estimates and their uncertainty. The joint model developed here sets the stage for future work to incorporate constraints, range measurements, and robust statistical modeling.