Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach

Stahlke M, Yammine G, Feigl T, Eskofier B, Mutschler C (2023)

Publication Language: English

Publication Status: Published

Publication Type: Journal article, Original article

Future Publication Type: Journal article

Publication year: 2023

Journal

IEEE Transactions on Machine Learning in Communications and Networking Institute of Electrical and Electronics Engineers (IEEE)

Publisher: IEEE

Series: Transactions on Machine Learning in Communications and Networking

Book Volume: 1

Pages Range: 1-15

Article Number: 1

Conference Proceedings Title: IEEE Vehicular Technology Conference (VTC-Spring)

Journal Issue: 1

DOI: 10.1109/TMLCN.2023.3256964

Abstract

Fingerprinting (FP) significantly improves the indoor localization performance in non-line-of-sight-dominated areas. However, its deployment and maintenance is cost-intensive as it needs ground-truth reference systems for both the initial training and the adaption to environmental changes. Recently, channel charting (CC) has been investigated because it is an unsupervised method that does not need labeled measurement data. CC uses pairwise-distance metrics to estimate the physical distance between channel-state-information (CSI) measurements in the proximity. CC can then learn the underlying manifold of the channel measurements. While CC has shown promising results in modelling the local geometry of the radio environment, a deeper insight into CC for localization using multi-anchor large-bandwidth measurements is still pending. We therefore contribute a novel distance metric for CC that approaches a global linear correlation to the physical distance based on time-synchronized single-input/single-output (SISO) CSIs. This allows to learn the environment's global geometry in a channel chart without annotations. We leverage a Siamese network, which enables CC-assisted FP only using a linear transformation from the chart to the real-world coordinates. We compare our approach to the state-of-the-art of CC on two different real-world data sets recorded with a 5G and UWB radio setup. Unlike FP-based localization which needs a large number of labeled data points to achieve a superior localization accuracy, our approach outperforms FP when only a few labeled data samples are available.

Authors with CRIS profile

Maximilian Stahlke Machine Learning and Data Analytics Lab Tobias Feigl Lehrstuhl für Informatik 2 (Programmiersysteme) Björn Eskofier Machine Learning and Data Analytics Lab Christopher Mutschler

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE) Universität Ulm DE Germany (DE)

How to cite

APA:

Stahlke, M., Yammine, G., Feigl, T., Eskofier, B., & Mutschler, C. (2023). Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach. IEEE Transactions on Machine Learning in Communications and Networking, 1(1), 1-15. https://dx.doi.org/10.1109/TMLCN.2023.3256964

MLA:

Stahlke, Maximilian, et al. "Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach." IEEE Transactions on Machine Learning in Communications and Networking 1.1 (2023): 1-15.

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