Maximilian Gaul

Advisors

Thomas Altstidl (M. Sc.), Maximilian Stahlke, Prof. Dr. Björn Eskofier

Duration

06 / 2023 – 12 / 2023

Abstract

Passive indoor localization using commercial off-the-shelf WiFi hardware [5] gained considerable interest over the recent years due to its wide range of possible use-cases such as an integration into smart-home, office- or show-room applications. Radio systems in general collect spatial information about reflection and diffraction that occur within a given channel. These so called channel state information (CSI) [4] were leveraged for people counting [8], indoor localization [10], tracking [11] and human activity recognition [3]. Recent advances in WiFi technology, namely WiFi 6(E), come with an increased number of usable subcarriers as well as an increased bandwidth per channel compared to the older WiFi 5 standard [1]. A higher channel bandwidth could result in more accurate sensing because of the higher time resolution of the measured CSI [9]. Ultra-broadband (UWB) modules leverage that property to achieve even higher temporal resolution of the radio signals [7] but they require special hard- and software and are not ubiquitously installed. Existing literature mostly employs hardware with restricted bandwidth of below 40 MHz [12, 2, 6, 8] with only one receiver and transmitter combination, which limits the ability for sensing tasks. Therefore, we want to compare the performance of recent radio standards such as WiFi 6(E) and UWB and evaluate the impact of varying channel bandwidth on people detection and counting as well as positioning by exploiting CSI measurements. To achieve this, we create scenarios for each application in an indoor office space where we setup multiple radio transceivers and perform offline data collection and training. Eventually, we define key performance indicators and evaluate the performance of different machine learning based algorithms on defined sensing and localization tasks with different levels of complexity.

References

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[3] Bing Li, Wei Cui, Wei Wang, Le Zhang, Zhenghua Chen, and Min Wu. Two-stream convolution augmented transformer for human activity recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1):286–293, May 2021.
[4] Yongsen Ma, Gang Zhou, and Shuangquan Wang. Wifi sensing with channel state information: A survey. ACM Comput. Surv., 52(3), jun 2019. [cs], Nov. 2022. [Online]. Available: http://arxiv.org/abs/2211. 10307 (visited on 12/19/2022).
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