Improved Automatic Electronic Intelligence Collection System for Internal and External Forward Fusion and Collaborative Geolocation of Adversary Emitters
Name
Benge_Final_Paper.pdf
Description
Technical Paper
Size
7.57 MB
Format
Adobe PDF
Checksum (MD5)
a9d9b60d53413e3245e2b2b7b13bfbde
Author(s)
Botero, Joey
Benge, Arianne
Heisey, Curtis
Date Issued
September 10, 2025
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%.
Subjects
LLSC
Machine Learning
The Department of the Air Force Artificial Intelligence Accelerator
MIT Lincoln Laboratory
Predictive Models
Signals
Electromagnetic Spectrum
Electronic Intelligence
MIT Department
Lincoln Laboratory
Persistent DSpace Link
