NEURAL NETWORKS FOR ADVANCED AAW TACTICAL DISPLAYS

THE AEGIS WEAPONS SYSTEM, WHICH BOASTS HIGHLY AUTOMATED DETECTION, TRACKING, AND ENGAGEMENT CAPABILITIES, PUSHED THE STATE OF THE ART IN 1980'S NAVAL AAW TECHNOLOGY. THE COMPLEXITIES OF NAVAL WARFARE IN THE COMING DECADE WILL ALMOST CERTAINLY REQUIRE THE INCLUSION OF ARTIFICIAL INTELLIGENCE (AI) TECHNIQUES IN FUTURE NAVAL SYSTEMS, A CAPABILITY THAT AEGIS LACKS. NEURAL NETWORK TECHNOLOGY, WITH ITS ABILITY TO RAPIDLY PROCESS NOISY MULTIVARIATE DATA, HOLDS GREAT PROMISE IN ENHANCING THE PROCESSING OF TACTICAL INFORMATION FOR LATER DISPLAY TO THE NAVAL DECISION MAKER. THIS PROPOSAL DESCRIBES A METHODOLOGY FOR USING A HYBRID NEURAL NET/EXPERT SYSTEM ENVIRONMENT TO CREATE AN INTELLIGENT TACTICAL DISPLAYS PREPROCESSOR. THE PROPOSED SYSTEM WILL INTEGRATE INFORMATION RECEIVED FROM MULTIPLE SENSORS, FILTER, AND PRIORITIZE THE INFORMATION PRIOR TO DISPLAY. THE SYSTEM WILL BE ABLE TO CORRELATE MULTIPLE TRACK FILES AND TO AUTOMATICALLY GROUP FORMATIONS OF AIRCRAFT TO DECLUTTER THE DISPLAY. ADDITIONALLY, INTELLIGENT THREAT PRIORITIZATION WILL BE IMPLEMENTED IN A CONTEXT-SENSITIVE MANNER. A COMPLETE PROTOTYPE SIMULATION OF THIS PROCESS WILL BE PROGRAMMED IN THE C LANGUAGE ON A MINICOMPUTER.