无预定义NLoS误差模型的鲁棒自适应无人机定位

IF 6.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Ruhul Amin Khalil;Junaid Bahadar Khan;Asiya Jehangir;Nasir Saeed
{"title":"无预定义NLoS误差模型的鲁棒自适应无人机定位","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":"{\"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}","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

摘要

在利用到达时间(ToA)测量的基于无人机的定位系统中,非视距(NLoS)条件带来了一个持续的挑战,它会引入显著的误差,降低定位精度。传统技术在很大程度上依赖于NLoS误差统计或测量噪声特性的先验知识。这些依赖关系使得这些方法的计算量很大,并且不太适合动态或大规模的场景。本文提出了一种低复杂度的定位算法,通过消除对先验NLoS错误统计或路径状态信息的需要来克服这些限制。该方法动态识别和排除受严重NLoS误差影响的ToA测量值,同时通过迭代更新提高定位精度。采用两阶段鲁棒回归算法(RRA),结合自适应无人机选择策略,保证了计算效率和精确定位。理论收敛分析验证了该算法在选择可靠的无人机和估计目标精确位置方面的鲁棒性。仿真结果表明,该算法与最先进的方法相比具有优越的性能,即使在严重的NLoS条件下也能实现更高的精度和效率。该方法的适应性、可扩展性和鲁棒性使其成为复杂动态环境(包括5G超密集网络和基于无人机的部署)中精确定位的有价值的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
13.70
自引率
3.80%
发文量
94
审稿时长
10 weeks
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信