Literature Database Entry
roesler2025improving
Sascha Rösler, Nils Erik Eising, Anatolij Zubow and Falko Dressler, "Improving WiFi Ranging Through Frequency Diversity and Mobility," Proceedings of 50th IEEE Conference on Local Computer Networks (LCN 2025), Sydney, Australia, October 2025. (to appear)
Abstract
Indoor positioning with IEEE 802.11 Fine Timing Measurement (FTM) relies on precise estimation of the Round Trip Time (RTT) between a pair of WiFi devices. However, studies have demonstrated that multi-path propagation affects FTM, which tends to overestimate and only rarely underestimates the actual distance. We present HopFTM to enhances FTM ranging accuracy by leveraging frequency diversity and space diversity through mobility. Specifically, HopFTM performs ranging on multiple radio channels. Additionally, in a mobile scenario, ranging is performed at multiple locations. Our experimental results using low-cost ESP32-S3 hardware reveal that selecting the smallest RTT value from all measured channels significantly improves accuracy, especially in environments with strong multi-path effects or non-line-of-sight conditions. In a static scenario by using the smallest RTT from four channels, we were able to reduce the median ranging error by 4× from 120 cm to 30 cm while the worst-case error was decreased from 15 m to 10 m, compared to traditional single-channel approach. In a mobile scenario, ranging measurements taken at different locations outside the decorrelation distance can also be fused. Our simulation shows that taking the smallest RTT value over multiple positions can improve FTM ranging error from 1.8 m to 0.76 m.
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Sascha Rösler
Nils Erik Eising
Anatolij Zubow
Falko Dressler
BibTeX reference
@inproceedings{roesler2025improving,
author = {R{\"{o}}sler, Sascha and Eising, Nils Erik and Zubow, Anatolij and Dressler, Falko},
note = {to appear},
title = {{Improving WiFi Ranging Through Frequency Diversity and Mobility}},
publisher = {IEEE},
address = {Sydney, Australia},
booktitle = {50th IEEE Conference on Local Computer Networks (LCN 2025)},
month = {10},
year = {2025},
}
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