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Positionsbestimmung mit IEEE 802.11az

Standorte per Deep Learning bestimmen

8. Mai 2023, 6:00 Uhr | Von Dr. Ahmad Saad, Florent Busnoult und Nadia Shivarova

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Fortsetzung des Artikels von Teil 3

Literatur

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[3] Kokkinis, A.; Kanaris, L.; Liotta, A. und Stavrou, S.: RSS Indoor Localization Based on a Single Access Point. Sensors, 2019, H. 17, 3711, https://doi.org/10.3390/s19173711.

[4] Wang, X.; Gao, L.; Mao, S. und Pandey, S.: CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach. IEEE Transactions on Vehicular Technology, 2017, H. 1, S. 763–776. https://doi.org/10.1109/TVT.2016.2545523.

[5] 802.11az Positioning Using Super-Resolution Time of Arrival Estimation. The MathWorks, Website, https://de.mathworks.com/help/wlan/ug/802-11az-indoor-positioning-using-super-resolution-time-of-arrival-estimation.html.

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[8] van de Beek, J. J.; et al.: On Channel Estimation in OFDM Systems. 1995 IEEE 45th Vehicular Technology Conference. Countdown to the Wireless Twenty-First Century, Konferenzband, S. 815–819.

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