Improved Automatic Electronic Intelligence Collection System for Internal and External Forward Fusion and Collaborative Geolocation of Adversary Emitters

Abstract

With the 2022 National Defense Strategy shifting focus from counterinsurgency operations to near-peer adversaries, airborne ISR platforms within the USAF and DoD must be improved for effectiveness in a near-peer conflict. They need to be able to operate quickly and effectively in contested environments with longer-range threats, act as a forward edge intelligence node for blue forces and provide DoD Research and Development efforts with cutting-edge data regarding new adversary signals and technology. To aid in tackling these challenges, this project introduces a Machine Learning (ML)-driven approach that revamps the Automatic Electronic Intelligence Collection System (ACS) on U.S. Airborne ISR platforms in four ways: First, by providing nodal analysis to the user in real time by automatically aggregating existing data across the aircraft to the user for decreased operator cognitive load. Second, increasing internal aircraft database information with external intelligence database information to increase confidence in targeting. Third, by providing automatic signal anomaly detection to the operator utilizing a support vector machines algorithm that cues operators to potential signals of interest based on previous activity and pattern of life prediction. Lastly, by providing better surface against airborne identification through utilization of cone angle to the system to help operators with faster threat warning and situational awareness of the environment. Findings include Support Vector Machines being the most effective tested binary classifier for predicting single signal anomaly detection at 84% AUC and a rule-based method of averages successfully classifying 1089 surface versus air ELINT samples with a success rate of 89% compared to other tested methods, such as Gaussian Mixture Models at 68% and KNearest Neighbor at 66%.