Torpedo Self-Defense Using Networked Automated Machine Intelligence (TSUNAMI)

US Navy efforts to develop effective torpedo countermeasures have yielded mixed results, but improvements are on the horizon. However, the relatively small number of sensors on each ship limits the military utility of the torpedo defense picture currently available to ship commanders. For effective torpedo defense given the current realities of sensor deployments, commanders need a system that integrates inputs from all networked ships in a strike group to detect, classify, and localize torpedo threats. Such a system will then be able to provide a comprehensive and consistent tactical picture that will: (1) generate torpedo threat alerts, (2) reduce risks to friendly units, and (3) permit optimization of counter-fire in response to a torpedo attack. Charles River Analytics is pleased to propose an information fusion system for Torpedo Self-Defense Using Networked Automated Machine Intelligence (TSUNAMI). TSUNAMI will automate the detection, classification, and localization of attacking torpedoes by combining relevant data from self-defense systems of the ship and other platforms.