Adaptive Gridding in Complex Physical Environments to Reduce Uncertainty

Effective Naval warfare depends on accurate estimates of sensor performance in complex environments, which provides a force multiplier by allowing friendly forces to optimize their tactics while avoiding counterdetection. The shift in emphasis from open ocean to littoral operations has increased the demands on models of sonar performance because data must be sampled at an order-of-magnitude finer resolution to obtain the same accuracy, which makes many models impractical in time-sensitive operations. A traditional response to this problem has been to replace detailed physical simulations with simpler, sensor specific, heuristics, but this approach is inflexible and generally provides inferior predictions. The solution to the problem of obtaining accurate sonar performance estimates in time to meet operational constraints is to run detailed physical models at a carefully selected set of grid points, and then to extrapolate from this small dataset to any other location/depth/frequency of interest. In this Phase II project we will enhance our Phase I Adaptive Gridding algorithm to select a minimal number of such grid points which will allow the Adaptive Gridding algorithm to make accurate predictions of sonar effectiveness throughout an operational area, and also further optimize its performance.