Predictions of AcousticS with Smart Experimental Networks of GlidERS (PASSENGERS)

Project Period: 2021-05-01 to 2024-04-30
Sponsor: Office of Naval Research (ONR) Task Force Ocean

The goal of this project is to improve the capability of data-assimilative ocean models to predict acoustic propagation in dynamic oceanography environments. This ambitious experimental research program will pair numerical models with adaptive-sampling gliders to optimize combinations of modeling and observational resources and predict acoustics sufficiently for operational applications. Our work will lay the groundwork for a significant advancement in the fidelity of the acoustic soundscape available to undersea vehicles upon which they rely to complete their missions. Our study includes real-time testing and comparison of several novel and diverse data assimilation and adaptive sampling techniques that minimize acoustic uncertainty. We will contrast starkly different ocean data-assimilation approaches – the use of coordinated teams of gliders and the most advanced algorithms on land vs. onboard algorithms paired with fresh data from sensors on the vehicle. Simultaneously, we will collect a paired acoustic and oceanographic data set that will be ideally suited to evaluate and compare novel state estimation and machine learning approaches both on land and at sea.