Robust localization in modern cellular networks using global map features
Chen, Junshi LU ; Li, Xuhong LU ; Whiton, Russ ; Leitinger, Erik and Tufvesson, Fredrik LU
(2025)
- Abstract
- Radio frequency (RF) signal-based localization using modern cellular networks has emerged as a promising solution to accurately locate objects in challenging environments. One of the most promising solutions for situations involving obstructed-line-of-sight (OLoS) and multipath propagation is multipathbased simultaneous localization and mapping (MP-SLAM) that employs map features (MFs), such as virtual anchors. This paper presents an extended MP-SLAM method that is augmented with a global map feature (GMF) repository. This repository stores consistent MFs of high quality that are collected during prior traversals. We integrate these GMFs back into the MP-SLAM framework via a probability hypothesis density (PHD) filter, which propagates GMF... (More)
- Radio frequency (RF) signal-based localization using modern cellular networks has emerged as a promising solution to accurately locate objects in challenging environments. One of the most promising solutions for situations involving obstructed-line-of-sight (OLoS) and multipath propagation is multipathbased simultaneous localization and mapping (MP-SLAM) that employs map features (MFs), such as virtual anchors. This paper presents an extended MP-SLAM method that is augmented with a global map feature (GMF) repository. This repository stores consistent MFs of high quality that are collected during prior traversals. We integrate these GMFs back into the MP-SLAM framework via a probability hypothesis density (PHD) filter, which propagates GMF intensity functions over time. Extensive simulations, together with a challenging real-world experiment using LTE RF signals in a dense urban scenario with severe multipath propagation and inter-cell interference, demonstrate that our framework achieves robust and accurate localization, thereby showcasing its effectiveness in realistic modern cellular networks such as 5G or future 6G networks. It outperforms conventional proprioceptive sensor-based localization and conventional MP-SLAM methods, and achieves reliable localization even under adverse signal conditions. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/188f08c4-1697-40af-95f7-5a7080b0027f
- author
- Chen, Junshi
LU
; Li, Xuhong
LU
; Whiton, Russ
; Leitinger, Erik
and Tufvesson, Fredrik
LU
- organization
- publishing date
- 2025
- type
- Working paper/Preprint
- publication status
- published
- subject
- pages
- 15 pages
- language
- English
- LU publication?
- yes
- id
- 188f08c4-1697-40af-95f7-5a7080b0027f
- alternative location
- https://arxiv.org/abs/2509.10433
- date added to LUP
- 2026-01-04 23:26:11
- date last changed
- 2026-01-07 11:20:43
@misc{188f08c4-1697-40af-95f7-5a7080b0027f,
abstract = {{Radio frequency (RF) signal-based localization using modern cellular networks has emerged as a promising solution to accurately locate objects in challenging environments. One of the most promising solutions for situations involving obstructed-line-of-sight (OLoS) and multipath propagation is multipathbased simultaneous localization and mapping (MP-SLAM) that employs map features (MFs), such as virtual anchors. This paper presents an extended MP-SLAM method that is augmented with a global map feature (GMF) repository. This repository stores consistent MFs of high quality that are collected during prior traversals. We integrate these GMFs back into the MP-SLAM framework via a probability hypothesis density (PHD) filter, which propagates GMF intensity functions over time. Extensive simulations, together with a challenging real-world experiment using LTE RF signals in a dense urban scenario with severe multipath propagation and inter-cell interference, demonstrate that our framework achieves robust and accurate localization, thereby showcasing its effectiveness in realistic modern cellular networks such as 5G or future 6G networks. It outperforms conventional proprioceptive sensor-based localization and conventional MP-SLAM methods, and achieves reliable localization even under adverse signal conditions.}},
author = {{Chen, Junshi and Li, Xuhong and Whiton, Russ and Leitinger, Erik and Tufvesson, Fredrik}},
language = {{eng}},
note = {{Preprint}},
title = {{Robust localization in modern cellular networks using global map features}},
url = {{https://arxiv.org/abs/2509.10433}},
year = {{2025}},
}