Toward fine-grained indoor localization based on massive MIMO-OFDM system : experiment and analysis
- Author
- Chenglong Li, Sibren De Bast, Emmeric Tanghe (UGent) , Sofie Pollin and Wout Joseph (UGent)
- Organization
- Project
- Abstract
- Fine-grained indoor localization has attracted attention recently because of the rapidly growing demand for indoor location-based services (ILBS). Specifically, massive (large-scale) multiple-input and multiple-output (MIMO) systems have received increasing attention due to high angular resolution. This paper presents an indoor localization testbed based on a massive MIMO orthogonal frequency-division multiplexing (OFDM) system, which supports physical-layer channel measurements. Instead of exploiting channel state information (CSI) directly for localization, we focus on positioning from the perspective of multipath components (MPCs), which are extracted from the CSI through the space-alternating generalized expectation-maximization (SAGE) algorithm. On top of the available MPCs, we propose a generalized fingerprinting system based on different single-metric and hybrid-metric schemes. We evaluate the impact of the varying antenna topologies, the size of the training set, the number of antennas, and the effective signal-to-noise ratio (SNR). The experimental results show that the proposed fingerprinting method can achieve centimeter-level positioning accuracy with a relatively small training set. Specifically, the distributed uniform linear array obtains the highest accuracy with about 1.63-2.5-cm mean absolute errors resulting from the high spatial resolution.
- Keywords
- Antennas, Massive MIMO, Antenna measurements, Location awareness, Linear, antenna arrays, Sensors, Measurement, Massive multiple-input and, multiple-output (MIMO), indoor localization, fingerprinting, multipath, components, channel state information (CSI), orthogonal, frequency-division multiplexing (OFDM), machine learning
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8772988
- MLA
- Li, Chenglong, et al. “Toward Fine-Grained Indoor Localization Based on Massive MIMO-OFDM System : Experiment and Analysis.” IEEE SENSORS JOURNAL, vol. 22, no. 6, 2022, pp. 5318–28, doi:10.1109/JSEN.2021.3111986.
- APA
- Li, C., De Bast, S., Tanghe, E., Pollin, S., & Joseph, W. (2022). Toward fine-grained indoor localization based on massive MIMO-OFDM system : experiment and analysis. IEEE SENSORS JOURNAL, 22(6), 5318–5328. https://doi.org/10.1109/JSEN.2021.3111986
- Chicago author-date
- Li, Chenglong, Sibren De Bast, Emmeric Tanghe, Sofie Pollin, and Wout Joseph. 2022. “Toward Fine-Grained Indoor Localization Based on Massive MIMO-OFDM System : Experiment and Analysis.” IEEE SENSORS JOURNAL 22 (6): 5318–28. https://doi.org/10.1109/JSEN.2021.3111986.
- Chicago author-date (all authors)
- Li, Chenglong, Sibren De Bast, Emmeric Tanghe, Sofie Pollin, and Wout Joseph. 2022. “Toward Fine-Grained Indoor Localization Based on Massive MIMO-OFDM System : Experiment and Analysis.” IEEE SENSORS JOURNAL 22 (6): 5318–5328. doi:10.1109/JSEN.2021.3111986.
- Vancouver
- 1.Li C, De Bast S, Tanghe E, Pollin S, Joseph W. Toward fine-grained indoor localization based on massive MIMO-OFDM system : experiment and analysis. IEEE SENSORS JOURNAL. 2022;22(6):5318–28.
- IEEE
- [1]C. Li, S. De Bast, E. Tanghe, S. Pollin, and W. Joseph, “Toward fine-grained indoor localization based on massive MIMO-OFDM system : experiment and analysis,” IEEE SENSORS JOURNAL, vol. 22, no. 6, pp. 5318–5328, 2022.
@article{8772988,
abstract = {{Fine-grained indoor localization has attracted attention recently because of the rapidly growing demand for indoor location-based services (ILBS). Specifically, massive (large-scale) multiple-input and multiple-output (MIMO) systems have received increasing attention due to high angular resolution. This paper presents an indoor localization testbed based on a massive MIMO orthogonal frequency-division multiplexing (OFDM) system, which supports physical-layer channel measurements. Instead of exploiting channel state information (CSI) directly for localization, we focus on positioning from the perspective of multipath components (MPCs), which are extracted from the CSI through the space-alternating generalized expectation-maximization (SAGE) algorithm. On top of the available MPCs, we propose a generalized fingerprinting system based on different single-metric and hybrid-metric schemes. We evaluate the impact of the varying antenna topologies, the size of the training set, the number of antennas, and the effective signal-to-noise ratio (SNR). The experimental results show that the proposed fingerprinting method can achieve centimeter-level positioning accuracy with a relatively small training set. Specifically, the distributed uniform linear array obtains the highest accuracy with about 1.63-2.5-cm mean absolute errors resulting from the high spatial resolution.}},
author = {{Li, Chenglong and De Bast, Sibren and Tanghe, Emmeric and Pollin, Sofie and Joseph, Wout}},
issn = {{1530-437X}},
journal = {{IEEE SENSORS JOURNAL}},
keywords = {{Antennas,Massive MIMO,Antenna measurements,Location awareness,Linear,antenna arrays,Sensors,Measurement,Massive multiple-input and,multiple-output (MIMO),indoor localization,fingerprinting,multipath,components,channel state information (CSI),orthogonal,frequency-division multiplexing (OFDM),machine learning}},
language = {{eng}},
number = {{6}},
pages = {{5318--5328}},
title = {{Toward fine-grained indoor localization based on massive MIMO-OFDM system : experiment and analysis}},
url = {{http://doi.org/10.1109/JSEN.2021.3111986}},
volume = {{22}},
year = {{2022}},
}
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