Autonomous Environmental Sensor Performance Prediction Tool for Multi-Static Active and Passive Anti-Submarine Warfare (ASW) Systems

The need exists for an autonomous Tactical Decision Aid (TDA) tool which can be used to optimize the effectiveness of Multi-Static Anti-Submarine Warfare (ASW) mission performance. The tool must make use of the most up-to-date and accurate environmental data obtained in real-time over a network interface. These environmental data include access to existing Naval Oceanographic Office environmental databases, in-situ measurements, and previous predictions performed under the same or similar environmental conditions. Without such a tool, the ASW mission commander has very little information with which to determine how various operational settings affect the outcome of the ASW mission. We propose a system, Autonomous Multi-static ASW Prediction System (AMAPS), that will achieve optimization levels exceeding Navy expectations by using available environmental data, improved ocean models, parallel programming enhancements, sophisticated decomposition of the optimization problem into an efficient algorithm, database archiving method, and implemented with a scalable design architecture that accommodates future enhancements from hardware improvements, new models, expanded datasets, fusion with other sensor types, and emergence of new threats.