COUNTERING SMALL UNMANNED AIRCRAFT SYSTEMS WITH ADVANCED DATA ANALYSIS AND MACHINE LEARNING
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Authors
Miske, Robert
Subjects
small unmanned aircraft system
sUAS
UAS
unmanned aerial vehicle
UAV
counter unmanned aircraft system
C-UAS
C-sUAS
drone
counter-drone
radar
detection
classification
discrimination
machine learning
supervised learning
unsupervised learning
data analysis
air defense
strategy
feature engineering
model robustness
sUAS
UAS
unmanned aerial vehicle
UAV
counter unmanned aircraft system
C-UAS
C-sUAS
drone
counter-drone
radar
detection
classification
discrimination
machine learning
supervised learning
unsupervised learning
data analysis
air defense
strategy
feature engineering
model robustness
Advisors
Yoshida, Ruriko
Date of Issue
2023-03
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
In January 2021, the DOD released its first Counter-Small Unmanned Aircraft Systems Strategy to address the growing risk to military personnel, facilities, and assets posed by the rapid technological advancement and proliferation of sUAS. Existing counter-drone capabilities—heavily reliant on electronic warfare to disrupt the communication link between user and device—no longer address an evolving threat that includes autonomous drones, COTS technology, and an increasing number of drones in the airspace that can overwhelm a C-sUAS operator. To counter the increasingly complex small drone threat, the Army-led Joint Counter-sUAS Office is pursuing materiel and non-materiel solutions for its new system-of-systems approach. One vexing C-sUAS challenge involves radar detection systems discriminating some sUAS from other flying objects, like birds, due to their comparable size, slow movement, and low altitude. Inaccurate or inefficient sUAS classification using radar data can be a force protection threat due to the limited number of electro-optical sensors and human operators for classification at-scale. This thesis uses bird and drone radar track data from two different training environments to explore hidden structure in the data, develop independent unsupervised and supervised learning models using the two datasets, and experiment with data sampling and feature engineering to improve upon model robustness to different environments and dynamic environmental conditions.
Type
Thesis
Description
Series/Report No
Department
Operations Research (OR)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.