Cost-Effective Mission Planning for Persistent Surveillance of the Littoral Physical Environment

Effective antisubmarine warfare (ASW) requires knowledge of all aspects of the complicated undersea environment. Variability of the oceanographic temperature field affects the sound speed structure and, therefore, acoustic propagation. With the development of dynamic ocean models and long-life Autonomous Undersea Vehicles for ocean sampling, the navy has a need for an adaptive environmental measurement mission planner (EMMP). Using knowledge of shallow water oceanography, ocean modeling and acoustic propagation for ASW operations, a non-linear global optimization (Genetic Algorithm) approach will be used to determine the optimal sensor suite and sampling scheme for cost-effective ocean characterization. The goal of the measurement system is to determine the mean state of the ocean (for sonar performance prediction and ASW mission planning) and to compute the associated environmental uncertainty due to ocean variability and to sub-optimal sampling. The final modules of the EMMP will include a pre-mission planner, an automatic controller with adaptive updates for during mission optimization, and a post-mission evaluation approach to estimate the resolution of the measurements and the associated uncertainty. The model ocean, with assimilated measurements, from the Autonomous Ocean Sampling Network experiment in Monterey Bay will be used as the "true ocean" for algorithm development, application and verification.