SBIR-STTR Award

The littoral environment is especially demanding on tactical sonar systems, in large part because the spatial and temporal variability imposes sonar system operating conditions of a nature and with a scale heretofore not encountered in the open oceans. Recent Office of Naval Research (ONR) sponsored basic research as well as fleet exercises have shown that littoral environments tactically important to the US Navy have rapidly varying sound speed profiles (direction and time dependent) along with directional wind-wave conditions and horizontally anisotropic bottom properties that result in spatial and temporal anisotropies in transmission loss. Most existing TL models within fleet Tactical Development Aids (TDAs) and System Performance Prediction (SPP) capabilities are accurate when the inputs are appropriately modeled and accounted for. But often there are not enough sensors in the water to sample the temporal and spatial variability sufficiently and the model outputs are inaccurate. This proposal addresses these deficiencies in acoustic models by leveraging highly-sampled GFI measurements of oceanographic variability to inform sonar detection uncertainty estimates and predictions of sensor-level output, to aid asset placement, enhance test planning, and improve accuracy of post-test reconstruction.

Benefit:
The validation of acoustic system performance prediction tools in the presence of oceanographic environmental uncertainty is of vital importance to the U. S. Navy. The successful completion of the Phase I and II work may lead to several beneficial commercial applications in the Government and private sectors arising from potential dual-use applications to those federal, state, or municipal agencies concerned with acoustic system performance prediction, and interpretation of measurements for accurate post-test reconstruction. The environmental uncertainty models to be developed herein are expected to be useful to the oceanographic community, mission planners, marine mammal monitoring concerns, as well as the oil exploration industry. Our strategy is enhanced by our connection and partnership with MITs Professor Lermusiaux, a well-known expert in modeling ocean variability and uncertainty. We expect that MIT and OASIS will partner through the entire Phase 1 through Phase 3 commercialization stages of the STTR program. OASIS chose MIT for this program because it has the computational capabilities and infrastructure required to successfully transition from first order ocean-acoustic models to a technologically advanced, cost-effective fielded system available to both commercial and Government users.

Keywords:
Transmission Loss (TL), Transmission Loss (TL), Tactical Decision Aids (TDA), Surface Duct (SD), Spatio-Temporal Variability, Detection Performance, uncertainty, Horizontal Anisotropy, Predictive Probability of Detection (PPD)

The littoral environment is especially demanding on tactical sonar systems, in large part because the spatial and temporal variability imposes sonar system operating conditions of a nature and with a scale heretofore not encountered in the open oceans. Recent Office of Naval Research (ONR) sponsored basic research as well as fleet exercises have shown that littoral environments tactically important to the US Navy have rapidly varying sound speed profiles (direction and time dependent) along with directional wind-wave conditions and horizontally anisotropic bottom properties that result in spatial and temporal anisotropies in transmission loss. Most existing TL models within fleet Tactical Development Aids (TDAs) and System Performance Prediction (SPP) capabilities are accurate when the inputs are appropriately modeled and accounted for. But often there are not enough sensors in the water to sample the temporal and spatial variability sufficientlyand the model outputs are inaccurate. This proposal addresses these deficiencies in acoustic models by leveraging highly-sampled GFI measurements of oceanographic variability to inform sonar detection uncertainty estimates and predictions of sensor-level output, to aid asset placement, enhance test planning, and improve accuracy of post-test reconstruction.