Literature Database Entry

hawarie2026automated

Abdul Hawarie, "Automated multi-channel FTM data collection for ML," Bachelor Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), June 2026. (Advisor: Sascha Rösler; Referees: Falko Dressler and Odej Kao)

Abstract

Indoor localization is an important technology for many modern applications, suchas mob ile robotics and indoor navigation. WiFi Fine Time Measurements (FTM) is an active research topic due to its availability and low cost. This thesis investigates the problem of inconsistent FTM distance estimation in indoor environments. Single-channel FTM measurements can vary strongly between channels and environments, which makes them unreliable as a direct distance estimator. Previous work addressed this problem by selecting the minimum RTT across multiple frequencies. This strategy is motivated by the assumption that multipath propagation mainly causes positive distance errors, and that the smallest measured Round Trip Time (RTT) is therefore less affected by overestimation. However, the selected minimum value is not guaranteed to be correct. It can still contain residual overestimation, and if the used hardware tends to underestimate the distance, selecting the minimum value can further increase the error. Therefore, this thesis investigates whether multi-channel FTM measurements can be combined more effectively than by using the minimum value alone. To address this problem, an autonomous measurement platform was developed to collect WiFi FTM measurements with Ultra-Wideband (UWB) reference distances under Line of Sight (LoS) conditions. Based on the collected data, an ML-based evaluation pipeline was implemented to analyze different multi-channel FTM fusion strategies. Statistical baselines such as the minimum RTT across channels were compared with linear regression models using different channel subsets and different models representations. The evaluated regression models included positive and unconstrained weights, both with and without intercept. The best indoor performance was achieved using an RTT-sorted linear regression model with intercept. By using only 3 FTM channels, this model reduced the indoor Root Mean Squared Error (RMSE) by approximately 48% compared to the minimum RTT baseline. Overall, the results show that multi-channel FTM can improve indoor distance estimation when the channel measurements are combined carefully. This Improvement depends on the training data, the hardware used for FTM and the ranging environment.

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Abdul Hawarie

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@phdthesis{hawarie2026automated,
    author = {Hawarie, Abdul},
    title = {{Automated multi-channel FTM data collection for ML}},
    advisor = {R{\"{o}}sler, Sascha},
    institution = {School of Electrical Engineering and Computer Science (EECS)},
    location = {Berlin, Germany},
    month = {6},
    referee = {Dressler, Falko and Kao, Odej},
    school = {TU Berlin (TUB)},
    type = {Bachelor Thesis},
    year = {2026},
   }
   
   

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Last modified: 2026-07-13