Intelligent Terrestrial EMI Emitter Locator for AFSCN Ground Stations based on AI Techniques
TRACER (Terrestrial RFI-locating Automation with CasE based Reasoning) is an AI-based, automated, electro magnetic interference (EMI) emitter localization and identification system. TRACER investigates suspected terrestrial sources of EMI, presents Air Force personnel with an intuitive description and classification of each EMI incursion and its current impact on operations, and recommends which steps should be taken to mitigate the interference.
To detect EMI, TRACER monitors signals from Remote Tracking Station and Directional Finder antennas around the world, via an interface to ALPS, a system developed by Intelligent Software Solutions (ISS). Upon first confirming or excluding the presence of space-based sources of interference, TRACER—based on details of the signal such as its classification (as determined by neural network technology developed by Data Fusion & Neural Networks (DF&NN), direction, and strength—retrieves and implements one or more investigative methodology cases. TRACER leverages Behavior Transition Networks from Stottler Henke’s SimBionic to rapidly and continuously update the probabilities of various possible sources of EMI. For instance, should one hypothesis be that the EMI originates from an aircraft, a first bearing and estimated distance can be plotted on Google Earth, a real-time flight tracking site (such as FlightAware) accessed, and the flights in the air at the time of the first EMI data point plotted. When the earth-based antenna has made its second sweep, the amount of bearing change confirms or refutes the hypothesis.
TRACER will enhance Space Situational Awareness (SSA), offer increased support for adversarial scenarios (both real and during training), and dramatically shorten EMI response time—and, in so doing, realize significant manpower savings.
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