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Indoor localization and tracking in reconfigurable intelligent surface aided mmWave systems

Indoor localization and tracking in reconfigurable intelligent surface aided mmWave systems
Indoor localization and tracking in reconfigurable intelligent surface aided mmWave systems
Millimeter wave (mmWave) carriers have a high available bandwidth, which can be beneficial for high-resolution localization in both the angular and temporal domains. However, the limited coverage due to severe path loss and line-of-sight (LoS) blockage are considered to be major challenges in mmWave. A promising solution is to employ reconfigurable intelligent surfaces (RIS) to circumvent the lack of line-of-sight paths, which can assist in localization. Furthermore, radio localization and tracking are capable of accurate real-time monitoring of the UE's locations and trajectories. In this paper, we propose a three-stage indoor tracking scheme. In the first stage, channel sounding is harnessed in support of the transmitter beamforming and receiver combining design. Based on the estimation in the first stage, a simplified received signal model is obtained, while using a discrete Fourier transform (DFT) matrix for the configuration of the RIS phase shifter for each time block. Based on the simplified received signal model, tracking initialization is carried out. Finally, in the third stage, Kalman filtering is employed for tracking. Our results demonstrate that the proposed scheme is capable of improving both the accuracy and robustness of tracking compared to single-shot successive localization. Additionally, we derive the position error bounds (PEB) of single-shot localization.
channel estimation, localization/positioning, mmWave, reconfigurable intelligent surfaces, sparse Bayesian learning, tracking
2644-1330
1815-1831
Li, Kunlun
412d655a-669d-4a41-9d7e-797649a845ed
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Xu, Chao
5710a067-6320-4f5a-8689-7881f6c46252
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Li, Kunlun
412d655a-669d-4a41-9d7e-797649a845ed
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Xu, Chao
5710a067-6320-4f5a-8689-7881f6c46252
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Li, Kunlun, El-Hajjar, Mohammed, Xu, Chao and Hanzo, Lajos (2025) Indoor localization and tracking in reconfigurable intelligent surface aided mmWave systems. IEEE Open Journal of Vehicular Technology, 6, 1815-1831. (doi:10.1109/OJVT.2025.3582885).

Record type: Article

Abstract

Millimeter wave (mmWave) carriers have a high available bandwidth, which can be beneficial for high-resolution localization in both the angular and temporal domains. However, the limited coverage due to severe path loss and line-of-sight (LoS) blockage are considered to be major challenges in mmWave. A promising solution is to employ reconfigurable intelligent surfaces (RIS) to circumvent the lack of line-of-sight paths, which can assist in localization. Furthermore, radio localization and tracking are capable of accurate real-time monitoring of the UE's locations and trajectories. In this paper, we propose a three-stage indoor tracking scheme. In the first stage, channel sounding is harnessed in support of the transmitter beamforming and receiver combining design. Based on the estimation in the first stage, a simplified received signal model is obtained, while using a discrete Fourier transform (DFT) matrix for the configuration of the RIS phase shifter for each time block. Based on the simplified received signal model, tracking initialization is carried out. Finally, in the third stage, Kalman filtering is employed for tracking. Our results demonstrate that the proposed scheme is capable of improving both the accuracy and robustness of tracking compared to single-shot successive localization. Additionally, we derive the position error bounds (PEB) of single-shot localization.

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More information

Accepted/In Press date: 21 June 2025
Published date: 25 June 2025
Keywords: channel estimation, localization/positioning, mmWave, reconfigurable intelligent surfaces, sparse Bayesian learning, tracking

Identifiers

Local EPrints ID: 503585
URI: http://eprints.soton.ac.uk/id/eprint/503585
ISSN: 2644-1330
PURE UUID: 51b84a40-f7bf-455d-bd8d-0f1b1fbcacfa
ORCID for Kunlun Li: ORCID iD orcid.org/0000-0002-5797-6560
ORCID for Mohammed El-Hajjar: ORCID iD orcid.org/0000-0002-7987-1401
ORCID for Chao Xu: ORCID iD orcid.org/0000-0002-8423-0342
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 05 Aug 2025 16:57
Last modified: 11 Sep 2025 03:45

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Contributors

Author: Kunlun Li ORCID iD
Author: Mohammed El-Hajjar ORCID iD
Author: Chao Xu ORCID iD
Author: Lajos Hanzo ORCID iD

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