@article {stevens_ocean_2023,
	title = {Ocean Ensemble-Enabled Stochastic Acoustic Prediction With Operational Metrics: New England Shelf Break Acoustics Signals and Noise Experiment},
	journal = {IEEE Journal of Oceanic Engineering},
	year = {2023},
	note = {Conference Name: IEEE Journal of Oceanic Engineering},
	pages = {1{\textendash}28},
	abstract = {This article describes the results of the New England shelf break acoustics (NESBA) experiment as they pertain to acoustic prediction and the quantification of associated uncertainties in relevant operational metrics. The uncertainties considered here are those due to the imperfect sensing of the water column, ambient noise (AN), and the seabed, and the impact this has on ocean forecasting and acoustic performance prediction accuracy. Operational metrics are designed to provide an acoustic system operator with actionable guidance relating to likely mean source detection ranges and associated uncertainties with specific guidance on the degree to which specific environmental factors (e.g., oceanography, seabed, and ambient noise) contribute to the predicted uncertainty levels. High-resolution regional Navy Coastal Ocean Model (NCOM) ensemble forecasts were generated to capture oceanographic variability and uncertainty. Passive ambient noise-based seabed measurements were conducted to estimate seabed properties. Extensive AN and conductivity, temperature, and depth (CTD) measurements were also conducted. Measurement-based versus model-based acoustic prediction metrics are compared as an initial validation of the underlying methodology. It is shown that Global Hybrid Coordinate Ocean Model (HYCOM) ocean forecasts with databased AN and seabed parameters result in very large metric uncertainties, while leveraging high-resolution NCOM with ensembles and in situ AN and seabed measurements can result in substantially reduced uncertainties. It is also demonstrated that improved ocean modeling and sensing can be leveraged to determine the best receiver depth, associated uncertainty levels, and uncertainty drivers. An operational concept for generating acoustic prediction metrics and associated operator environmental sensing guidance is proposed.},
	keywords = {acoustics, Cumulative probability of detection, Measurement, passive sonar, Predictive models, Sea measurements, Sea surface, stochastic acoustic prediction, Temperature measurement, uncertainty},
	issn = {1558-1691},
	doi = {10.1109/JOE.2023.3267788},
	author = {Stevens, William K. and Siderius, Martin and Carrier, Matthew J. and Lin, Ying-Tsong and Wendeborn, Drew}
}
@article {stevens_optimally_2023,
	title = {Optimally Distributed Receiver Placements Versus an Environmentally Aware Source: New England Shelf Break Acoustics Signals and Noise Experiment},
	journal = {IEEE Journal of Oceanic Engineering},
	year = {2023},
	note = {Conference Name: IEEE Journal of Oceanic Engineering},
	abstract = {This article describes the results of the Spring of 2021 New England Shelf Break Acoustics (NESBA) Signals and Noise experiment as they pertain to the optimization of a field of passive receivers versus an environmentally aware source with end-state goals. A discrete optimization has been designed and used to demonstrate providing an acoustic system operator with actionable guidance relating to optimally distributed receiver locations and depths and likely mean source detection times and associated uncertainties as a function of source and receiver levels of environmental awareness. The uncertainties considered here are those due to the imperfect spatial and temporal sensing of the water column, ambient noise (AN), and the seabed, and the impact this has on ocean forecasting and acoustic performance prediction accuracy. As a part of the NESBA experiment, high-resolution (1 km spatial) regional Navy Coastal Ocean Model ensemble forecasts were generated to capture oceanographic variability and uncertainty. Passive AN-based seabed measurements were conducted to estimate seabed properties including variability and uncertainty. Extensive AN and conductivity, temperature, and depth measurements were also conducted. In this article, operationally relevant metrics are employed to estimate the potential value-added of optimal receiver location and depth placements as a function of source end-state goals and assumed level of environmental awareness. A concept for generating stochastic acoustic prediction metrics and associated optimally distributed receiver locations and depths in an operational environment is proposed.},
	issn = {1558-1691},
	doi = {10.1109/JOE.2023.3302915},
	url = {https://ieeexplore.ieee.org/document/10251950},
	author = {Stevens, William K. and Siderius, Martin and Carrier, Matthew J. and Wendeborn, Drew}
}
