ORCID Number
0000-0002-5880-3629
Date of Award
Summer 7-13-2026
Access Type
Dissertation - Open Access
Degree Name
Doctor of Philosophy in Electrical Engineering & Computer Science
Department
Electrical, Computer, Software, and Systems Engineering
Committee Chair
Bryan C. Watson
Committee Chair Email
watsonb3@erau.edu
Committee Co-Chair
Radu F. Babiceanu
Committee Co-Chair Email
radu.babiceanu@wmich.edu
First Committee Member
Omar Ochoa
First Committee Member Email
ochoao@erau.edu
Second Committee Member
Richard S. Stansbury
Second Committee Member Email
stansbur@erau.edu
Third Committee Member
Yongxin Liu
Third Committee Member Email
liuy11@erau.edu
College Dean
James W. Gregory
Abstract
The rapid growth of Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) is creating a new low-altitude airspace ecosystem where drones, air taxis, service suppliers, communication networks, sensors, and ground-based monitoring systems must work together safely. Within this ecosystem, UAS Traffic Management (UTM) is expected to provide a digital framework for coordinating operations beyond traditional air traffic control. However, reliable integration also requires resilient monitoring methods that can detect non-cooperative aircraft, protect communication links, and maintain timely situational awareness under real-world constraints.
This dissertation examines how computer vision can support cooperative monitoring systems such as Remote ID and ADS-B by detecting and tracking cooperative and non-cooperative UAS from visual data while accounting for the security and network constraints that affect information delivery. The study establishes a replay-based evaluation framework for assessing perception-derived UAS monitoring outputs, including detection performance, tracking continuity, conflict-detection timeliness, payload expansion, and end-to-end latency. The evaluation uses curated UAS video scenarios across different traffic-density conditions (low, medium and high) and examines system behavior under baseline, security, network-sensitivity, and combined worst-case configurations.
Further, the dissertation evaluates how cybersecurity mechanisms, including encryption and authentication, affect monitoring-message size and end-to-end latency. It also analyzes how bandwidth limitation, network latency, packet loss, and combined degraded conditions influence the usefulness of vision-based monitoring outputs. The findings show that security overhead is manageable under favorable communication conditions, while bandwidth limitation and packet loss create more significant challenges for timely and reliable monitoring. The results also demonstrate that combined stress conditions can create greater degradation than individual constraints alone, especially when secured payloads are transmitted over constrained links. Overall, this dissertation provides assessment-driven evidence for integrating computer vision, secure communication, and network-aware design into future UTM and AAM monitoring environments. It highlights the value of vision-based monitoring as an additional awareness layer for cooperative and non-cooperative UAS traffic, while also identifying the communication and security conditions under which such monitoring remains operationally useful.
Scholarly Commons Citation
Issoufou Anaroua, Fadjimata, "Assessing Computer Vision Based Conflict Detection in UAS Traffic Monitoring Under Secure Communication Constraints" (2026). Doctoral Dissertations and Master's Theses. 1010.
https://commons.erau.edu/edt/1010
Included in
Aviation Safety and Security Commons, Digital Communications and Networking Commons, Multi-Vehicle Systems and Air Traffic Control Commons, Other Computer Engineering Commons, Systems and Communications Commons, Systems Engineering Commons
