| 标 题： |
5G Direct Position Estimation for Precise Localization in Dense Urban Area
5G直接定位估计在密集城市区域中的精确定位 |
| 作 者： |
Sijia Li
Sergio Vicenzo
Bing Xu |
| 关键词： |
5G positioning [5G定位]
Maximum likelihood estimator [ 最大似然估计器]
Urban navigation [ 城市导航] |
| 出版年： |
2025 |
| 文集名： |
2025 IEEE/ION Position, Location and Navigation Symposium (PLANS 2025)
2025年IEEE/ION位置、定位与导航研讨会（PLANS 2025） |
| 会议名： |
2025 IEEE/ION Position, Location and Navigation Symposium (PLANS 2025)
2025年IEEE/ION位置、定位与导航研讨会（PLANS 2025） |
| 届 次： |
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| 摘 要： |
In recent years, the fifth-generation (5G) new radio (NR) signals have emerged as a promising supplementary resource for urban navigation. However, a major challenge in utilizing 5G signals lies in their vulnerability to non-line-of-sight (NLoS) propagation effects, which are especially prevalent in urban street canyons. This paper applies the direct position estimation (DPE) method to 5G cellular signals to mitigate the NLoS bias as well as the multipath effects, thereby enabling precise localization in urbanized environments. The feasibility of applying the DPE method to NR positioning is analyzed, followed by a discussion of the tapped delay line (TDL) channel propagation model provided by the 3rd Generation Partnership Project (3GPP). The positioning performance is then evaluated through large-scale system-level simulations. The simulation results demonstrate that 5G DPE achieves satisfactory positioning accuracy in a 10 dB noisy channel, with an overall root mean square error (RMSE) constrained within 6 m. In addition, 5G DPE outperforms the observed time difference of arrival (OTDoA) method by 95.24% in terms of positioning accuracy in an NLoS-dominated propagation environment.
近年来，第五代（5G）新无线电（NR）信号作为城市导航的潜在补充资源逐渐受到关注。然而，利用5G信号面临的主要挑战在于其对非视距（NLoS）传播效应的敏感性，这种效应在城市街道峡谷中尤为明显。本文将直接位置估计（DPE）方法应用于5G蜂窝信号，以缓解NLoS偏差和多径效应，从而实现城市环境中的精确定位。文章分析了DPE方法在NR定位中的可行性，并讨论了第三代合作伙伴计划（3GPP）提供的分段延迟线（TDL）信道传播模型。随后通过大规模系统级仿真评估了定位性能。仿真结果表明，在10 dB噪声信道下，5G DPE可实现令人满意的定位精度，整体均方根误差（RMSE）控制在6米以内。此外，在以非视距（NLoS）为主的传播环境中，5G DPE在定位精度方面比观测到的到达时间差（OTDoA）方法高出95.24%。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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