SBIR-STTR Award

The ASSETT Team, comprised of ASSETT, Inc., Texas A&Ms Dr. Smotherman and MNW Associates, intends to develop a machine learning process to improve performance of high frequency sonar. Our added learning process incorporating knowledge base, learning mode, and feedback from enhanced monitoring will enable the sonar to emulate the echolocation behavior of bats and dolphin. Dolphin have no problem in shallow littorals with complex bathymetry, non iso-velocity, and non-isotropic environments that challenge current high frequency sonar effectiveness. The ASSETT Team will apply mammalian adaptive behavior to improve operational dependability of high frequency sonars in these challenging environments.

Benefit:
Current high frequency SSN and AUV sonars with a finite repertoire of waveforms, transmission rates, range scales, and source levels are challenged by the littorals. Our added knowledge base, learning mode and real-time feedback processes replaces the finite repertoire with one that continuously adapts to changes in the brown water environment. This processing improvement emulates the adaptive echolocation behavior of marine mammals that survive in these complex brown water environments and improves mission effectiveness in the littorals. Attributes of the adapting process will be updated as more discoveries related to mammalian echolocation are made.

Keywords:
High Frequency Sonar, High Frequency Sonar, Machine Learning, mammalian sonar, echolocation of bats and dolphin, Littorals