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Autonomous Sensing UAV for Accurate Multi-User Identification and Localization in LAWN

Niccolò Paglierani, Francesco Linsalata, Vineeth Teeda, Davide Scazzoli, Maurizio Magarini

TL;DR

The results confirm the feasibility of infrastructure-independent sensing UAVs as a core element of the emerging Low Altitude Economy (LAE), supporting situational awareness and rapid deployment in emergency or connectivity-limited environments.

Abstract

This paper presents an autonomous sensing framework for identifying and localizing multiple users in Fifth Generation (5G) cooperative networks using an Unmanned Aerial Vehicle (UAV) that is not part of the serving access network. Unlike conventional aerial serving nodes, the proposed UAV operates passively and is dedicated solely to sensing. Passively receiving Uplink (UL) Sounding Reference Signals (SRS), the UAV requires only minimal initial coordination with the network infrastructure during the mission. A complete signal processing chain is proposed and developed, encompassing synchronization, user identification, and localization, all executed onboard UAV during flight. The system autonomously plans and adapts its mission workflow to estimate multiple user positions within a single deployment, integrating flight control with real-time sensing. The approach is validated through extensive simulations and a full-scale low-altitude experimental campaign. Urban simulation scenarios show localization errors below 8 m, while rural field tests achieve errors below 3 m, with reliable synchronization and user identification ensured in both cases. The results confirm the feasibility of infrastructure-independent sensing UAVs as a core element of the emerging Low Altitude Economy (LAE), supporting situational awareness and rapid deployment in emergency or connectivity-limited environments.

Autonomous Sensing UAV for Accurate Multi-User Identification and Localization in LAWN

TL;DR

The results confirm the feasibility of infrastructure-independent sensing UAVs as a core element of the emerging Low Altitude Economy (LAE), supporting situational awareness and rapid deployment in emergency or connectivity-limited environments.

Abstract

This paper presents an autonomous sensing framework for identifying and localizing multiple users in Fifth Generation (5G) cooperative networks using an Unmanned Aerial Vehicle (UAV) that is not part of the serving access network. Unlike conventional aerial serving nodes, the proposed UAV operates passively and is dedicated solely to sensing. Passively receiving Uplink (UL) Sounding Reference Signals (SRS), the UAV requires only minimal initial coordination with the network infrastructure during the mission. A complete signal processing chain is proposed and developed, encompassing synchronization, user identification, and localization, all executed onboard UAV during flight. The system autonomously plans and adapts its mission workflow to estimate multiple user positions within a single deployment, integrating flight control with real-time sensing. The approach is validated through extensive simulations and a full-scale low-altitude experimental campaign. Urban simulation scenarios show localization errors below 8 m, while rural field tests achieve errors below 3 m, with reliable synchronization and user identification ensured in both cases. The results confirm the feasibility of infrastructure-independent sensing UAVs as a core element of the emerging Low Altitude Economy (LAE), supporting situational awareness and rapid deployment in emergency or connectivity-limited environments.

Paper Structure

This paper contains 27 sections, 39 equations, 13 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. System Model
  4. Scenario and operational workflow
  5. Sounding Reference Signals (SRS)
  6. Received Signal Model
  7. UEs synchronization, identification, and localization
  8. Coarse Time Synchronization via SRS Structure
  9. Fine Synchronization and UE Identification
  10. Separation of Signals
  11. Peaks Extraction
  12. User Identification via Cyclic Shift
  13. Fine synchronization
  14. Measurement Cleaning and False Positive Handling
  15. UEs Localization
  16. ...and 12 more sections

Figures (13)

  • Figure 1: Operational scenario where a sensor UAV acts as non-serving receiver to localize transmitting signals towards the network.
  • Figure 2: Overview of the proposed framework building blocks.
  • Figure 3: Illustration of the mapping: (left) placement on the grids with $K_{TC}=2$, and (right) the corresponding repeated structure in the time domain.
  • Figure 4: OFDM window for (top) an unsynchronized receiver and (bottom) a synchronized receiver.
  • Figure 5: Impact of delay spread on the synchronization detection metric.
  • ...and 8 more figures