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

Effective detection, classification, and localization of submarine-launched torpedoes are critical to protect US naval forces operating in harm"s way. 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 available to ship Commanders. Commanders need an integrated torpedo defense system that includes inputs from all networked ships in a strike group to provide a comprehensive and consistent tactical picture that will (1) generate torpedo threat alerts, (2) reduce risk to friendly units, and (3) permit optimization of counter-fire in response to a torpedo attack. The major tasks of the anti-torpedo system are detection, classification, and localization. Charles River Analytics is pleased to propose an information fusion system for Torpedo Self-Defense Using Networked Automated Machine Intelligence (TSUNAMI) that will automate the detection, classification, and localization of torpedoes by combining relevant data from self-defense systems of the ship and other platforms.