Research Summaries

Back Machine Learning based own Ship Acoustic Monitoring (OSAM)

Fiscal Year 2021
Division Graduate School of Operational & Information Sciences
Department Computer Science
Investigator(s) Orescanin, Marko
Sponsor Office of Naval Research (Navy)
Summary Understanding real-time acoustic signature of submarines can ensure stealth operation in adversary waters increasing mission success and platform survivability. Present techniques used in evaluating submarines own acoustic signature have not scaled with the technology and are not able to keep up with the large volumes of "raw" data being produced from onboard sensors. Opportunity exists to revisit automation of acoustic signature monitoring in light of new developments in the Artificial Intelligence and Machine Learning. These disruptive technologies have been proven in commercial environment to provide actionable information while reducing false-alarm rates and cognitive load of the operators.
Keywords Artificial Intelligence machine intelligence machine learning
Publications Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal
Data Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal