{"title":"Robust and Adaptive UAVs-Based Localization Without Predefined NLoS Error Models","authors":"Ruhul Amin Khalil;Junaid Bahadar Khan;Asiya Jehangir;Nasir Saeed","doi":"10.1109/OJCOMS.2025.3564497","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4051-4062"},"PeriodicalIF":6.3000,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10977048","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Communications Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10977048/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023.
The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include:
Systems and network architecture, control and management
Protocols, software, and middleware
Quality of service, reliability, and security
Modulation, detection, coding, and signaling
Switching and routing
Mobile and portable communications
Terminals and other end-user devices
Networks for content distribution and distributed computing
Communications-based distributed resources control.