DATA FUSION ARCHITECTURES FOR UNDERSEA WARFARE WITH AUTONOMOUS UNDERWATER VEHICLES

Abstract

This capstone report examines the concept of combining mobile and stationary underwater sensors into a coherent, distributive network. This project presents a baseline architecture for a data fusion system that facilitates the near real-time exchange of information from disparate sources. This architecture, in turn, provides a basis for further system development, and guides future studies of relevant data/information fusion concepts and technologies for applications to anti-submarine warfare (ASW) and mine warfare. This study uses the unique approach of inverse systems engineering to design an architecture based on the ASW kill chain, and the probability of success in detecting, classifying and tracking underwater objects. The probability of success is then measured against the same probability of success of a human ASW operator to determine the adequacy of design. The team utilized ExtendSim software to model and simulate the architecture to validate functional capability and improved performance over the human ASW operator. The resulting architecture facilitates the successful integration of passive acoustic sensor information with intelligence products and timely distribution of fused data across manned and unmanned platforms. The architecture also allows for future growth into active acoustic sources, environmental data sources, non-traditional ASW sources such as radar, and ESM.