THIS PROJECT WILL ASSESS ENEMY INFORMATION-ATTACK CYCLE TIMES FOR THE SMALL MOBILE MISSILE SYSTEM.

THIS PROJECT WILL ASSESS ENEMY INFORMATION-ATTACK CYCLE TIMES FOR THE SMALL MOBILE MISSILE SYSTEM. ANALYSIS WILL FOCUS ON DISCERNIBLE SIGNATURES OF THE MISSILE SYSTEM, INTERRELATIONSHIPS BETWEEN SIGNATURES, AND THE RELATIONSHIP OF SIGNATURES TO OPERATIONAL FACTORS. SIGNATURES WHICH ARE VULNERABLE TO ENEMY CY 1990-2000 DETECTION, TECHNOLOGIES WILL BE ANALYZED FOR DETECTION-IDENTIFICATION PROBABILITIES AND CYCLE TIMES FOR AN INFORMATION ATTACK SEQUENCE. WHILE PROVIDING A BASIS FOR SYSTEM MOVEMENT CRITERIA, THE ANALYSIS WILL ALSO DETERMINE THE FEASIBILITY OF AN ARTIFICIAL INTELLIGENCE (AI) VULNERABILITY ASSESSMENT MODEL. THIS INNOVATIVE MODEL WOULD, DURING THE DESIGN-DEVELOPMENT PHASE OF SYSTEM ACQUISITION, PROVIDE AN ASSESSMENT OF THE IMPACTS OF DESIGN ALTERNATIVES AND TRADE-OFFS ON SURVIVABILITY.THROUGH THE SELF-LEARNING ATTRIBUTES OF AI, THE MODEL COULD BE "GROWN"INTO A "VULNERABILITY MONITOR" TO CONTINUALLY SENSE FIELDED SYSTEM VULNERABILITY TO DETECTION-IDENTIFICATION.