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

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## Abstract

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## 1. Introduction

- Edge computing.
- Vehicle to Everything communication (V2X).
- Beamforming.
- Multi-array antenna.

- Investigating the algorithms for RF-based localisation that are highly likely to be used for challenging UGV and UAV applications.
- Reviewing the existing works considered using RF specifically for UAVs and UGVs positioning.
- Discussing the new potential that 5G NR will provide to cope with the current issues in UAVs and UGVs localisation problem.
- Discussing the challenges for ground and aerial robots localisation and its integration with 5G NR.

## 2. RF Features

#### 2.1. Received Signal Strength

- ${P}_{0}$: power at the reference distance ${d}_{0}$ from the transmitter (usually 1m).
- ${P}_{d}$: received power at distance d from the transmitter.
- ${X}_{\sigma}$: shadowing effect( mostly considered as Gaussian).
- $\alpha $: Path loss exponent (PLE), the rate at which power decrease over distance.
- b: bias error.

#### 2.2. Time of Arrival

#### 2.3. Time Difference of Arrival

#### 2.4. Angle of Arrival

#### 2.5. Channel System Information

## 3. Overview of RF-Based Localisation Techniques

- Range-based techniques: Localisation is achieved by inferring the distance or angle of the target from a node based on the measurements. Time Of Arrival (TOA), Time Difference Of Arrival (TDOA), and Received Signal Strength (RSS) provide ranges, while Angle Of Arrival (AOA) provides bearings measurements. In a sensor framework, two or several of these methods can be combined, which might result in a better outcome. In the next stage, the extracted ranges or bearings are used to estimate the location, taking advantage of various mathematical tools such as Maximum Likelihood (ML), the Least Squares (LS) approach, the Bayesian model, or different types of filters such as the Kalman filter (KF), extended Kalman (EKF), Unscented Kalman filter (UKF), and Particle filter (PF). In the next section, we will explain the methods used for range-based localisation.
- Range-free or Fingerprinting: Instead of calculating the distance or direction, the environmental survey is performed to obtain fingerprints or features recorded on a database, such as the location-RSS pair’s value, and then in online mode, for every new measurement, the localisation is performed by finding the best match in the data set. More generally, this method consists of mapping and matching. Compared to the range-based approach, fingerprinting techniques are more accurate and demanding to implement, requiring a pretest to create an extensive database. In addition, fingerprinting methods differ in generating and updating the data set and the matching process. Nevertheless, fingerprinting is widely used for CSI and RSS-based localisation.

## 4. Range-Based Algorithms

#### 4.1. Multi-Lateration/Triangulation

#### 4.2. Min–Max

#### 4.3. Multidimensional Scaling (MDS)

#### 4.4. Least Squares (LS)

#### 4.5. Maximum Likelihood (ML)

#### 4.6. Bayesian Inference Method

#### 4.7. Bayesian Filters

## 5. Fingerprinting

#### 5.1. Offline Step

#### 5.2. Online Phase

#### 5.2.1. Classical Machine Learning

#### 5.2.2. Deep Learning

## 6. Other Taxonomies

#### 6.1. Distributed vs. Centralised

#### 6.2. Cooperative vs. Non-Cooperative

#### 6.3. Anchor-Based vs. Anchor-Free

#### 6.4. Static vs. Mobile

#### 6.5. Technologies

#### 6.6. 2D vs. 3D

#### 6.7. Performance Parameters

## 7. RF-Based Localisation for Aerial and Ground Robots

- UGVs, and especially UAVs, are highly manoeuvrable, with high speed. The existing state of the art for WSN localisation focuses on fixed targets and cannot address the rapid changes in the target location and the real-time implementation.
- The mobility of vehicles calls for a combination of other sensors, such as IMU and Images. The combination of the sensor data, especially images and RF, has not been studied in localisation.
- The majority of current works in WSN often consider just 2D cases, while vertical estimation is of great importance in UAV localisation.
- The accuracy and robustness in demand in UAVs and UGVs localisation applications are more critical. Usually, very accurate estimation is necessary, while in WSN, rather rough estimation suffices. This, for instance, rules out relying merely on RSS, which is the case for most of the existing state-of-the-art RF localisation.
- Use of limited technologies is the other drawback. For robot applications, UWB is used most. It is limited to indoors and is suitable for short range. New Technologies, especially 5G NR, have rarely been considered so far. In 5G, RSS would not be the most relevant feature, so there would be a shift to the use of this technology’s new potentials and capabilities.

^{2}means squared error.

- Limited to specific technologies and sensor data: Most papers use RSS due to its easy-to-use hardware. In that case, acceptable accuracy is achieved by using UWB, which is limited for indoor use with short range. TOA-based localisation is also achieved mostly by taking advantage of UWB. Moreover, many possibilities are missing in the literature, such as the integration of images, and LIDAR with RFs.
- Limited Accuracy: accuracy is one of the main concerns in UAVs and UGVs localisation. Only relying on simple algorithms and sensor data, like RSS, might not be an appropriate solution, especially with the upcoming technologies, 5G and beyond. As we discuss later in our paper, CSI information would provide a huge amount of useful data. However, the real-time implementation and its fusion with conventional sensor data is the real concern that is not addressed. Edge computing and off-loading as the most promising solutions are rarely investigated.

## 8. 5G Potentials and Promises for Robot Applications

- Wide area coverage.
- MIMO technology.
- High carrier frequency.
- High bandwidth.
- Vehicle-to-Everything (V2X).
- Low latency.
- High throughput.

#### 8.1. Wide Area Coverage and Inexpensive Localisation Systems

#### 8.2. RF Measurements with More Resolution

#### 8.3. Vehicle-to-Everything Standard

#### 8.4. Low Latency

#### 8.5. High Throughput

#### 8.6. Localisation Based on 5G

## 9. Future Research Directions and Challenges

#### 9.1. Fingerprinting and Deep Learning Applied to CSI

#### 9.2. Fusion of RF with Other Sensor Data

#### 9.3. Combination of Multiple Estimators

#### 9.4. Cooperative Localisation

#### 9.5. Orientation Estimation

#### 9.6. Experimental Setup and Realistic Simulation

#### 9.7. Off-Loading

#### 9.8. Simultaneous Localisation and Mapping

#### 9.9. Vertical Localisation Accuracy

#### 9.10. Safety

## 10. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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Reference | Brief Summary | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|

[9] | A survey on RF localisation for UAV with focus on technologies and performance metrics | ✓ | ✗ | ✓ | ✗ |

[8] | A survey on IoT localisation, investigating technologies and performance metrics | ✗ | ✗ | ✓ | ✗ |

[10] | A comprehensive survey on localisation on WSN | ✗ | ✗ | ✓ | ✗ |

[15] | A overview of the 5G-based localisation | ✗ | ✗ | ✗ | ✓ |

[11] | A survey on IoT localisation | ✗ | ✗ | ✓ | ✗ |

[12] | A survey on localisation techniques on WSN | ✗ | ✗ | ✓ | ✗ |

[13] | A survey on localisation in WSN in 3D space | ✗ | ✗ | ✓ | ✗ |

[14] | A survey on localisation on WSN algorithm and techniques | ✗ | ✗ | ✓ | ✗ |

[16] | A brief review on range-free localisation algorithms in WSN | ✗ | ✗ | ✓ | ✗ |

[7] | A comprehensive survey of the indoor localisation using different technologies | ✗ | ✗ | ✓ | ✗ |

[17] | A brief review of technologies used for UAV positioning in indoor environment | ✓ | ✗ | ✗ | ✗ |

Range-Based Methods | Scenario | Advantages | Disadvantages |
---|---|---|---|

Multi-lateration Triangulation | fast and rough estimation scenarios | simple calculation | limited accuracy, sensitive to measurement error |

Min-Max | fast and rough estimation scenarios | low complexity, easy implementation | limited accuracy |

Multidimensional Scaling (MDS) | cooperative localisation | reduce the complexity | difficult to include the knowledge about unequal measurement error |

Least Square (LS) | high accuracy | easier implementation and less demanding than ML and Bayesian, gives estimation uncertainty | computationally demanding, less optimal compared to ML and Bayesian |

Maximum Likelihood (ML) | high accuracy, inaccurate prior information (outperform Bayesian) | gives estimation uncertainty | computationally demanding |

Bayesian Inference | higher accuracy, sparse observations | gives estimation uncertainty | computationally demanding (more demanding than LS and ML) |

Extended Kalman Filter (EKF) | real-time dynamic state estimation Easy implementation for real-time | simpler multi-sensor fusion, suitable for mobile targets, easy implementation, gives estimation uncertainty | not useful for non-Gaussian noise, less optimal compared to UKF and PF |

Unscented Kalman filter (UKF) | real-time dynamic state estimation, better accuracy compared to EKF | simpler multi-sensor fusion, suitable for mobile target, gives estimation uncertainty | not useful for non-gaussian noise |

Particle Filter (PF) | high accuracy dynamic state estimation | handling non-gaussian noise, gives estimation uncertainty | computationally demanding, difficult implementation |

Refs | Year | Range-Based/Fingerprinting | Distributed/Centralised | Cooperative/Non-Cooperative | Anchor-Based/Anchor-Free | 2D/3D | Experiment | Technique | Technology |
---|---|---|---|---|---|---|---|---|---|

[45] | 2014 | fingerprinting | centralised | non-cooperative | anchor-free | 2D | Yes | PF | WLAN/RSS |

[80] | 2010 | range-based | centralised | non-cooperative | anchor-based | 2D | Yes | UKF | -/RSS |

[81] | 2020 | range-based | centralised | non-cooperative | anchor-based | 3D | Yes | UKF | UWB/TOA |

[82] | 2019 | fingerprinting | centralised | non-cooperative | anchor-based | 2D | Yes | PF | ZigBee/RSS |

[83] | 2021 | fingerprinting | centralised | non-cooperative | anchor-free | 2D | Yes | PF-ML | WiFi/RSS |

[84] | 2018 | range-based | centralised | cooperative | anchor-based | 3D | No | EKF | WiFi-UWB/RSS-TOA |

[85] | 2008 | range-based | centralised | non-cooperative | anchor-based | 2D | No | EKF | -/TDOA |

[86] | 2009 | range-based | centralised | non-cooperative | anchor-based | 2D | No | EKF | -/TDOA |

[87] | 2018 | range-based | centralised | non-cooperative | anchor-based | 3D | Yes | LS | UWB/TOA |

[88] | 2016 | range-based | centralised | non-cooperative | anchor-based | 2D | Yes | SRCKF | -/RSS |

[89] | 2016 | range-based | distributed | cooperative | anchor-based | 2D | No | DEKF | -/AOA |

[90] | 2020 | fingerprinting | centralised | non-cooperative | anchor-free | 2D | Yes | PF | -/RSS |

[91] | 2021 | range-based | centralised | non-cooperative | anchor-based | 3D | No | ML | -/RSS |

[92] | 2022 | range-based | distributed | cooperative | anchor-based | 3D | No | ML | -/RSS |

[93] | 2021 | range-based | centralised | non-cooperative | anchor-based | 3D | Yes | EKF | UWB/TW-TOF |

[94] | 2021 | fingerprinting | centralised | cooperative | anchor-free | 2D | Yes | ML | WiFi/RSS |

[95] | 2010 | range-based | centralised | non-cooperative | anchor-based | 2D | Yes | Bayesian | -/RSS |

[96] | 2021 | range-based | centralised | non-cooperative | anchor-based | 2D | Yes | LS | Cellular/AOA |

[97] | 2017 | range-based | centralised | non-cooperative | anchor-based | 3D | No | MDS-WCL | WiFi/RSS |

[98] | 2020 | range-based | centralised | non-cooperative | anchor-based | 3D | No | Lateration | -/AOA |

[99] | 2020 | range-based | centralised | non-cooperative | anchor-based | 3D | No | LS | -/TDOA-AOA |

[100] | 2019 | range-based | centralised | non-cooperative | anchor-based | 2D | Yes | RLS | UWB/TOA |

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**MDPI and ACS Style**

Kabiri, M.; Cimarelli, C.; Bavle, H.; Sanchez-Lopez, J.L.; Voos, H.
A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives. *Sensors* **2023**, *23*, 188.
https://doi.org/10.3390/s23010188

**AMA Style**

Kabiri M, Cimarelli C, Bavle H, Sanchez-Lopez JL, Voos H.
A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives. *Sensors*. 2023; 23(1):188.
https://doi.org/10.3390/s23010188

**Chicago/Turabian Style**

Kabiri, Meisam, Claudio Cimarelli, Hriday Bavle, Jose Luis Sanchez-Lopez, and Holger Voos.
2023. "A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives" *Sensors* 23, no. 1: 188.
https://doi.org/10.3390/s23010188