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A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

Meisam Kabiri, Claudio Cimarelli, Hriday Bavle, Jose Luis Sanchez-Lopez, Holger Voos

TL;DR

This review surveys RF-based localization for UAVs and UGVs, detailing range-based and fingerprinting techniques and the RF features (RSS, TOA, TDOA, AOA, CSI) that enable them. It synthesizes the state-of-the-art algorithms, including LS, ML, Bayesian methods, and various filters, and discusses how 5G NR can enhance localization through richer measurements, edge computing, and V2X cooperation. The paper highlights 5G-specific opportunities and challenges, such as PRS/SRS-based positioning, CSI fingerprints, and the need for realistic experiments and SLAM fusion in GPS-denied settings. It also outlines future directions like CSI-driven fingerprinting with deep learning, multi-sensor fusion, cooperative localization, and robust vertical localization to make RF-based localization practical for autonomous aerial and ground robots.

Abstract

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored.

A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

TL;DR

This review surveys RF-based localization for UAVs and UGVs, detailing range-based and fingerprinting techniques and the RF features (RSS, TOA, TDOA, AOA, CSI) that enable them. It synthesizes the state-of-the-art algorithms, including LS, ML, Bayesian methods, and various filters, and discusses how 5G NR can enhance localization through richer measurements, edge computing, and V2X cooperation. The paper highlights 5G-specific opportunities and challenges, such as PRS/SRS-based positioning, CSI fingerprints, and the need for realistic experiments and SLAM fusion in GPS-denied settings. It also outlines future directions like CSI-driven fingerprinting with deep learning, multi-sensor fusion, cooperative localization, and robust vertical localization to make RF-based localization practical for autonomous aerial and ground robots.

Abstract

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored.
Paper Structure (49 sections, 7 equations, 7 figures, 3 tables)

This paper contains 49 sections, 7 equations, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. RF Features
  3. Received Signal Strength
  4. Time-Of-Arrival
  5. Time-Difference-Of-Arrival
  6. Angle-Of-Arrival
  7. Channel System Information
  8. Overview of RF-based Localization Techniques
  9. Range-based Algorithms
  10. Multi-Lateration/Triangulation
  11. Min-Max
  12. Multidimensional scaling (MDS)
  13. Least squares (LS)
  14. Maximum Likelihood (ML)
  15. Bayesian Inference Method
  16. ...and 34 more sections

Figures (7)

  • Figure 1: Trilateration: localization based on the range from 3 anchors (TOA, RSS).
  • Figure 2: Triangulation: localization based on AOA from 3 BS.
  • Figure 3: Localization based on TDOA, the intersection of the hyperbolas.
  • Figure 4: Min-Max algorithm.
  • Figure 5: Reference points in 3D.
  • ...and 2 more figures