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

Stahlke, Maximilian; Yammine, George; Feigl, Tobias; Eskofier, Bjoern M.; Mutschler, Christopher

doi:10.1109/tmlcn.2023.3256964

Cited by 12 publications

(3 citation statements)

References 51 publications

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“…Lastly, frequency difference of arrival (FDoOA) is possible, but is challenging for many waveforms, especially if they are from low-cost imprecise transmitters. For example, broadband indoor localization systems exploit supervised [ 73 ] or unsupervised [ 74 ] learning strategies for localization (regression of a global position) of user equipment, even in dynamic and multipath situations. Hence, localization is the next expansion of the monitoring system and preliminary performance is presented in this article (see Section 5.1 ).…”

Section: Background To Interference Monitoringmentioning

confidence: 99%

Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System

Merwe

Franco

Hansen

et al. 2023

Sensors

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Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference source must be detected, classified, its purpose determined, and localized to eliminate it. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This article introduces a low-cost commercial-off-the-shelf (COTS) GNSS interference monitoring, detection, and classification receiver. It employs machine learning (ML) on tailored signal pre-processing of the raw signal samples and GNSS measurements to facilitate a generalized, high-performance architecture that does not require human-in-the-loop (HIL) calibration. Therefore, the low-cost receivers with high performance can justify significantly more receivers being deployed, resulting in a significantly higher probability of intercept (POI). The architecture of the monitoring system is described in detail in this article, including an analysis of the energy consumption and optimization. Controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approaches. The ML results show that accurate and reliable detection and classification are possible with COTS hardware.

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Section: Background To Interference Monitoringmentioning

confidence: 99%

Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System

Merwe

Franco

Hansen

et al. 2023

Sensors

Self Cite

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“…Among CC methods, some explicitly rely on a distance measure between channels to build the chart [19], [20], [21], [22]. For them to be successful, the considered channel distance must allow to reliably detect if any two channels measurements were taken close to each other or not in a given area.…”

Section: Introductionmentioning

confidence: 99%

“…In [19], a correlation matrix distance is introduced to rule out channels with large azimuthal separation from the construction of a neighborhood graph, the edges of which are computed relying on the feature distance originally introduced in [6]. In [22], a multi-anchor time of flight (ToF) based distance is derived and exploited to construct the neighborhood graph. The geodesic distance resulting from it shows great correlation with the spatial Euclidean distance.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting

Yassine,

Magoarou,

Crussière

et al. 2024

IEEE Trans. Wireless Commun.

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Coded environments: data-driven indoor localisation with reconfigurable intelligent surfaces

Shah,

Shawky,

Kazim

et al. 2024

Commun Eng

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Reconfigurable Intelligent Surfaces have recently emerged as a revolutionary next-generation wireless networks paradigm that harnesses engineered electromagnetic environments to reshape radio wave propagation. Pioneering research presented in this article establishes the viability of Reconfigurable Intelligent Surfaces-enhanced indoor localisation and charts a roadmap for its integration into next-generation wireless network architectures. Here, we present a comprehensive experimental analysis of a Reconfigurable Intelligent Surfaces-enabled indoor localisation scheme that evaluates the localisation accuracy of different machine learning algorithms under varying Reconfigurable Intelligent Surfaces states, antenna types, and communication setups. The results indicate that incorporating Reconfigurable Intelligent Surfaces can significantly enhance indoor localisation accuracy, achieving an impressive 82.4% success rate. Moreover, this study delves into system performance across varied communication modes and subcarrier configurations. This research is poised to lay the groundwork for implementing Reconfigurable Intelligent Surfaces-empowered joint sensing and communications in future next-generation wireless networks.

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

Abstract: This work was supported by the Fraunhofer Lighthouse project "6G SENTINEL" and by the Federal Ministry of Education and Research of Germany in the programme of "Souver än. Digital. Vernetzt." joint project 6G-RIC (16KISK020K).

Cited by 12 publications

References 51 publications

Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System

Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System

Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting

Coded environments: data-driven indoor localisation with reconfigurable intelligent surfaces

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