Intelligent 5G-Aided UAV Positioning in High-Density Environments Using Neural Networks for NLOS Mitigation
The accurate and reliable positioning of unmanned aerial vehicles (UAVs) in urban environments is crucial for urban air mobility (UAM) application, such as logistics, surveillance, and disaster management. However, global navigation satellite systems (GNSSs) often fail in densely populated areas due...
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MDPI AG
2025-06-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/6/543 |
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| author | Morad Mousa Saba Al-Rubaye |
| author_facet | Morad Mousa Saba Al-Rubaye |
| author_sort | Morad Mousa |
| collection | DOAJ |
| description | The accurate and reliable positioning of unmanned aerial vehicles (UAVs) in urban environments is crucial for urban air mobility (UAM) application, such as logistics, surveillance, and disaster management. However, global navigation satellite systems (GNSSs) often fail in densely populated areas due to signal reflections (multipath propagation) and obstructions non-line-of-sight (NLOS), causing significant positioning errors. To address this, we propose a machine learning (ML) framework that integrates 5G position reference signals (PRSs) to correct UAV position estimates. A dataset was generated using MATLAB’s UAV simulation environment, including estimated coordinates derived from 5G time of arrival (TOA) measurements and corresponding actual positions (ground truth). This dataset was used to train a fully connected feedforward neural network (FNN), which improves the positioning accuracy by learning patterns between predicted and actual coordinates. The model achieved significant accuracy improvements, with a mean absolute error (MAE) of 1.3 m in line-of-sight (LOS) conditions and 1.7 m in NLOS conditions, and a root mean squared error (RMSE) of approximately 2.3 m. The proposed framework enables real-time correction capabilities for dynamic UAV tracking systems, highlighting the potential of combining 5G positioning data with deep learning to enhance UAV navigation in urban settings. This study addresses the limitations of traditional GNSS-based methods in dense urban environments and offers a robust solution for future UAV advancements. |
| format | Article |
| id | doaj-art-ab84e33a811b45aa9cfb4f8d03f4fa1a |
| institution | Matheson Library |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Aerospace |
| spelling | doaj-art-ab84e33a811b45aa9cfb4f8d03f4fa1a2025-06-25T13:19:32ZengMDPI AGAerospace2226-43102025-06-0112654310.3390/aerospace12060543Intelligent 5G-Aided UAV Positioning in High-Density Environments Using Neural Networks for NLOS MitigationMorad Mousa0Saba Al-Rubaye1School of Aerospace, Transportation and Manufacturing, Cranfield University, Cranfield MK43 0AL, UKSchool of Aerospace, Transportation and Manufacturing, Cranfield University, Cranfield MK43 0AL, UKThe accurate and reliable positioning of unmanned aerial vehicles (UAVs) in urban environments is crucial for urban air mobility (UAM) application, such as logistics, surveillance, and disaster management. However, global navigation satellite systems (GNSSs) often fail in densely populated areas due to signal reflections (multipath propagation) and obstructions non-line-of-sight (NLOS), causing significant positioning errors. To address this, we propose a machine learning (ML) framework that integrates 5G position reference signals (PRSs) to correct UAV position estimates. A dataset was generated using MATLAB’s UAV simulation environment, including estimated coordinates derived from 5G time of arrival (TOA) measurements and corresponding actual positions (ground truth). This dataset was used to train a fully connected feedforward neural network (FNN), which improves the positioning accuracy by learning patterns between predicted and actual coordinates. The model achieved significant accuracy improvements, with a mean absolute error (MAE) of 1.3 m in line-of-sight (LOS) conditions and 1.7 m in NLOS conditions, and a root mean squared error (RMSE) of approximately 2.3 m. The proposed framework enables real-time correction capabilities for dynamic UAV tracking systems, highlighting the potential of combining 5G positioning data with deep learning to enhance UAV navigation in urban settings. This study addresses the limitations of traditional GNSS-based methods in dense urban environments and offers a robust solution for future UAV advancements.https://www.mdpi.com/2226-4310/12/6/5435G networkspositioningneural networkTOAunmanned aerial vehiclesnon-line-of-sight (NLOS) |
| spellingShingle | Morad Mousa Saba Al-Rubaye Intelligent 5G-Aided UAV Positioning in High-Density Environments Using Neural Networks for NLOS Mitigation Aerospace 5G networks positioning neural network TOA unmanned aerial vehicles non-line-of-sight (NLOS) |
| title | Intelligent 5G-Aided UAV Positioning in High-Density Environments Using Neural Networks for NLOS Mitigation |
| title_full | Intelligent 5G-Aided UAV Positioning in High-Density Environments Using Neural Networks for NLOS Mitigation |
| title_fullStr | Intelligent 5G-Aided UAV Positioning in High-Density Environments Using Neural Networks for NLOS Mitigation |
| title_full_unstemmed | Intelligent 5G-Aided UAV Positioning in High-Density Environments Using Neural Networks for NLOS Mitigation |
| title_short | Intelligent 5G-Aided UAV Positioning in High-Density Environments Using Neural Networks for NLOS Mitigation |
| title_sort | intelligent 5g aided uav positioning in high density environments using neural networks for nlos mitigation |
| topic | 5G networks positioning neural network TOA unmanned aerial vehicles non-line-of-sight (NLOS) |
| url | https://www.mdpi.com/2226-4310/12/6/543 |
| work_keys_str_mv | AT moradmousa intelligent5gaideduavpositioninginhighdensityenvironmentsusingneuralnetworksfornlosmitigation AT sabaalrubaye intelligent5gaideduavpositioninginhighdensityenvironmentsusingneuralnetworksfornlosmitigation |