Robust and Adaptive UAVs-Based Localization Without Predefined NLoS Error Models
In UAV-based localization systems utilizing Time of Arrival (ToA) measurements, Non-Line-of-Sight (NLoS) conditions present a persistent challenge by introducing significant errors that degrade localization accuracy. Traditional techniques rely heavily on prior knowledge of NLoS error statistics or...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10977048/ |
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| author | Ruhul Amin Khalil Junaid Bahadar Khan Asiya Jehangir Nasir Saeed |
| author_facet | Ruhul Amin Khalil Junaid Bahadar Khan Asiya Jehangir Nasir Saeed |
| author_sort | Ruhul Amin Khalil |
| collection | DOAJ |
| description | In UAV-based localization systems utilizing Time of Arrival (ToA) measurements, Non-Line-of-Sight (NLoS) conditions present a persistent challenge by introducing significant errors that degrade localization accuracy. Traditional techniques rely heavily on prior knowledge of NLoS error statistics or measurement noise characteristics. These dependencies make such methods computationally intensive and less adaptable to dynamic or large-scale scenarios. This paper presents a low-complexity localization algorithm that overcomes these limitations by eliminating the need for prior NLoS error statistics or path status information. The proposed approach dynamically identifies and excludes ToA measurements affected by severe NLoS errors while refining localization accuracy through iterative updates. A two-stage Robust Regression Algorithm (RRA) is employed, combined with an adaptive UAV selection strategy, ensuring both computational efficiency and precise positioning. Theoretical convergence analysis verifies the algorithm’s robustness in selecting reliable UAVs and estimating the accurate position of the target. Simulation results show the algorithm’s superior performance compared to state-of-the-art methods, achieving higher accuracy and efficiency even under severe NLoS conditions. The proposed method’s adaptability, scalability, and robustness make it a valuable solution for accurate localization in complex and dynamic environments, including 5G ultra-dense networks and UAV-based deployments. |
| format | Article |
| id | doaj-art-626d8b995e4640d58e7eb8e8727c594e |
| institution | OA Journals |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-626d8b995e4640d58e7eb8e8727c594e2025-08-20T02:30:38ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0164051406210.1109/OJCOMS.2025.356449710977048Robust and Adaptive UAVs-Based Localization Without Predefined NLoS Error ModelsRuhul Amin Khalil0https://orcid.org/0000-0003-4039-9901Junaid Bahadar Khan1https://orcid.org/0000-0001-8877-6564Asiya Jehangir2Nasir Saeed3https://orcid.org/0000-0002-5123-5139Engineering Requirement Unit, College of Engineering, United Arab Emirates University, Al-Ain, UAEDepartment of Electrical Engineering, University of Engineering and Technology, Peshawar, PakistanDepartment of Electrical Engineering, University of Engineering and Technology, Peshawar, PakistanDepartment of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, Al-Ain, UAEIn UAV-based localization systems utilizing Time of Arrival (ToA) measurements, Non-Line-of-Sight (NLoS) conditions present a persistent challenge by introducing significant errors that degrade localization accuracy. Traditional techniques rely heavily on prior knowledge of NLoS error statistics or measurement noise characteristics. These dependencies make such methods computationally intensive and less adaptable to dynamic or large-scale scenarios. This paper presents a low-complexity localization algorithm that overcomes these limitations by eliminating the need for prior NLoS error statistics or path status information. The proposed approach dynamically identifies and excludes ToA measurements affected by severe NLoS errors while refining localization accuracy through iterative updates. A two-stage Robust Regression Algorithm (RRA) is employed, combined with an adaptive UAV selection strategy, ensuring both computational efficiency and precise positioning. Theoretical convergence analysis verifies the algorithm’s robustness in selecting reliable UAVs and estimating the accurate position of the target. Simulation results show the algorithm’s superior performance compared to state-of-the-art methods, achieving higher accuracy and efficiency even under severe NLoS conditions. The proposed method’s adaptability, scalability, and robustness make it a valuable solution for accurate localization in complex and dynamic environments, including 5G ultra-dense networks and UAV-based deployments.https://ieeexplore.ieee.org/document/10977048/LocalizationUAVstime of arrivalsemidefinite programmingmultidimensional scalingregression |
| spellingShingle | Ruhul Amin Khalil Junaid Bahadar Khan Asiya Jehangir Nasir Saeed Robust and Adaptive UAVs-Based Localization Without Predefined NLoS Error Models IEEE Open Journal of the Communications Society Localization UAVs time of arrival semidefinite programming multidimensional scaling regression |
| title | Robust and Adaptive UAVs-Based Localization Without Predefined NLoS Error Models |
| title_full | Robust and Adaptive UAVs-Based Localization Without Predefined NLoS Error Models |
| title_fullStr | Robust and Adaptive UAVs-Based Localization Without Predefined NLoS Error Models |
| title_full_unstemmed | Robust and Adaptive UAVs-Based Localization Without Predefined NLoS Error Models |
| title_short | Robust and Adaptive UAVs-Based Localization Without Predefined NLoS Error Models |
| title_sort | robust and adaptive uavs based localization without predefined nlos error models |
| topic | Localization UAVs time of arrival semidefinite programming multidimensional scaling regression |
| url | https://ieeexplore.ieee.org/document/10977048/ |
| work_keys_str_mv | AT ruhulaminkhalil robustandadaptiveuavsbasedlocalizationwithoutpredefinednloserrormodels AT junaidbahadarkhan robustandadaptiveuavsbasedlocalizationwithoutpredefinednloserrormodels AT asiyajehangir robustandadaptiveuavsbasedlocalizationwithoutpredefinednloserrormodels AT nasirsaeed robustandadaptiveuavsbasedlocalizationwithoutpredefinednloserrormodels |