This technology combines edge-diffraction modeling, boundary-element methods, and parallel processing to create a model of acoustic scattering of midfrequency active sonar from underwater targets that are larger than mines and smaller than conventional submarines. ARiA’s approach offers an accurate and efficient method for calculating acoustic scattering of midfrequency threats that is 10 times faster than current state-of-the-art target-scattering models and scales well at high frequencies. This method has been validated against third party BEM software and analytical solutions. ARiA provides research and development in interdisciplinary acoustics, signal processing, modeling & simulation, machine learning, and artificial intelligence. The goal is to transition this technology as a tool for ONR, NAVAIR, and NAVSEA to use in creating midfrequency synthetic target signatures for developing signal-processing algorithms.
Performance of detection and classification of targets in active sonar systems may be degraded in the presence of stationary clutter, ownship motion-induced clutter, and active interference. Applied Research in Acoustics’ (ARiA) sparse estimation algorithms estimate and separate targets, reverberation, and mutual interference signals from a cluttered signal and enable novel classification features to be computed from sparse representations. Integration of ARiA’s advanced signal and information processing enables automated and semi-automated sonar signal detection and classification, thus reducing operator workload. ARiA’s signal and information processing enhancements are targeted for the AN/SQQ89A(V)15 Integrated Undersea Warfare (USW) Combat System Suite’s pulsed active sonar (PAS) function segment (PASFS) echo tracker classifier (ETC). However, the developed algorithms are suitable for integration into most active sonar or radar platforms.