By providing a unified approach to modeling & simulation for training that leverages deep domain knowledge of acoustic modeling within a state-of-the-art machine-learning and knowledge-engineering framework, ARiA is developing training systems that enhance the effectiveness of our nation’s warfighters. Our current training projects include
Environment for Surface ASW Interactive Learning (ESAIL™)
In partnership with award-winning game company Filament Games, ARiA is developing a capability for interactive game-based education in underwater acoustics that incorporates real-time fidelity-adaptive modeling & simulation to realize both STEM education goals for secondary students and training goals for US Navy sonar operators. Together ARiA and Filament are producing WaveQuest™, which will be commercially available to students, parents, and educators.
Synthetic ASW Generation Engine (SAGE™)
The Synthetic ASW Generation Engine (SAGE™) is a fundamentally new approach to automatic scenario generation for simulation-based training. Under development for the ONR 321US High-Fidelity Active Sonar Training (HiFAST) EC effort, SAGE integrates an inference engine and ontology of ASW sonar training within an active semisupervised machine-learning framework to enable automatic generation of scenario parameters for fidelity-adaptive simulation-based ASW sonar training that jointly satisfy training objectives and computational-load constraints.
Effective signal processing in complex environments requires an understanding of the physical processes that generate noise and clutter in those environments.
By leveraging our work in modeling & simulation, ARiA is developing signal-processing techniques and algorithms that enable automation of detection, classification, and recognition that is robust in the most challenging environments. Our current signal-processing projects include
Deep Representation Learning from Sonar Big Data for Automatic Target Recognition
Topological Investigation of Target/Clutter Features in Mine-Countermeasures Sonar Data
ARiA is leading a team including mathematicians from American University to leverage recent developments in topological signal processing including algebraic-geometric methods and stratified-manifold learning toward development of new and insightful feature-space representations of scattering from targets and clutter. In addition to development of new feature representations for automatic target recognition (ATR), this effort is investigating the use of topological methods to characterize computational models used for simulation of mine-countermeasures (MCM) sonar systems, such as PC SWAT.