Management and Analysis of Localization Information in Uncrewed Aerial Systems

Reliable localization is a critical enabling function for autonomous uncrewed aerial vehicles (UAVs), particularly in environments where Global Navigation Satellite System (GNSS) signals are degraded or unavailable. This thesis investigates how 5G cellular networks - leveraging dense terrestrial infrastructure, precise timing, and recently standardized UAV oriented capabilities - can enhance localization performance, coordination, and mission reliability for both individual UAVs and cooperative swarms. First, we present a release-by-release analysis of 3GPP’s evolving support for UAVs from LTE Release 15 through 5G-Advanced Release 19, highlighting architectural, radio, and sidelink mechanisms that enable identification, 3D tracking, command-and-control, and integration with UAS Traffic Management systems, while also identifying key gaps and opportunities for future enhancements in UAV communication and localization. Second, we design and experimentally validate the first 5G-enabled UAV testbed developed at Wireless@VT, enabling controlled investigation of how communication latency affects swarm behavior. Using this platform, we demonstrate that the primary determinant of swarm responsiveness is the 5G numerology configuration—specifically, how increasing OFDM subcarrier spacing reduces transmission time intervals and air-interface latency. This reduction significantly improves the timeliness of localization-information exchange, enabling tighter formation keeping, faster synchronization, and overall behavior more consistent with the low-latency, high-reliability goals of URLLC-class services. Finally, we develop a localization-driven trajectory optimization framework that incorporates Position Error Bounds derived from the Fisher Information Matrix, enabling UAVs to identify and traverse geometrically favorable routes that reduce localization uncertainty by up to 30% without compromising communication performance. Together, these contributions demonstrate how standardized 5G communication and localization capabilities can practically enhance UAV autonomy and robustness in GNSS-challenged environments.

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Publication Details

Journal
VTechWorks (Virginia Tech)
Published
2025-11-24
Primary Topic
UAV Applications and Optimization
Type
article
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article

Management and Analysis of Localization Information in Uncrewed Aerial Systems

Anand Mahesh Kumar
VTechWorks (Virginia Tech)
UAV Applications and Optimization
article

Management and Analysis of Localization Information in Uncrewed Aerial Systems

Anand Mahesh Kumar
article en

Abstract

Reliable localization is a critical enabling function for autonomous uncrewed aerial vehicles (UAVs), particularly in environments where Global Navigation Satellite System (GNSS) signals are degraded or unavailable. This thesis investigates how 5G cellular networks - leveraging dense terrestrial infrastructure, precise timing, and recently standardized UAV oriented capabilities - can enhance localization performance, coordination, and mission reliability for both individual UAVs and cooperative swarms. First, we present a release-by-release analysis of 3GPP’s evolving support for UAVs from LTE Release 15 through 5G-Advanced Release 19, highlighting architectural, radio, and sidelink mechanisms that enable identification, 3D tracking, command-and-control, and integration with UAS Traffic Management systems, while also identifying key gaps and opportunities for future enhancements in UAV communication and localization. Second, we design and experimentally validate the first 5G-enabled UAV testbed developed at Wireless@VT, enabling controlled investigation of how communication latency affects swarm behavior. Using this platform, we demonstrate that the primary determinant of swarm responsiveness is the 5G numerology configuration—specifically, how increasing OFDM subcarrier spacing reduces transmission time intervals and air-interface latency. This reduction significantly improves the timeliness of localization-information exchange, enabling tighter formation keeping, faster synchronization, and overall behavior more consistent with the low-latency, high-reliability goals of URLLC-class services. Finally, we develop a localization-driven trajectory optimization framework that incorporates Position Error Bounds derived from the Fisher Information Matrix, enabling UAVs to identify and traverse geometrically favorable routes that reduce localization uncertainty by up to 30% without compromising communication performance. Together, these contributions demonstrate how standardized 5G communication and localization capabilities can practically enhance UAV autonomy and robustness in GNSS-challenged environments.

VTechWorks (Virginia Tech)
Industry, innovation and infrastructure
Openalex Percentile: Top 13%
UAV Applications and Optimization
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