Advancing indoor positioning compatibility: Uncooperative access points and one-sided RTT
Friman, Robert (2024)
2024
Teknis-luonnontieteellinen DI-ohjelma - Master's Programme in Science and Engineering
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2024-03-22
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202403072742
https://urn.fi/URN:NBN:fi:tuni-202403072742
Tiivistelmä
In the modern age of the global world, masses of people rely on accurate navigation and positioning services in their lives. Where global positioning system (GPS) has paved the way for accurate services outdoors, and especially in rural areas, indoor positioning has been neglected. Urban areas, being the early adopters of new high-tech innovations, present a compelling arena for the integration of advanced technologies.
As the demand for more accurate and reliable indoor positioning systems continues to grow, Wi-Fi Round-Trip Time (RTT) technology emerges as a promising solution. This Master’s Thesis investigates the potential of Wi-Fi RTT in enhancing location-based services (LBS) within indoor environments. The main aim of this research is the compatibility aspect of RTT with legacy devices.
Wi-Fi’s fine-time measurement (FTM) protocol, designed for indoor localization with high accuracy, relies on cooperative signal round-trip time (RTT) measurements between two devices. However, as of 2023, the adoption of the FTM protocol among commercially-deployed Wi-Fi access points (APs) remains limited. This thesis addresses this limitation by exploring a general solution for positioning by taking also the one-sided RTT measurements into consideration. This technique, which operates independently of AP cooperation, would enable mobile devices to obtain distance measurements with most existing APs.
This thesis covers two positioning methodologies: coverage area ellipse-based method and multilateration. Moreover, the mathematical concepts that make these methodologies possible are explained in detail, including the derivation of multivariate normal distribution and the Gauss-Newton method for minimization.
This research contributes to advancing the field of indoor positioning by providing promising accuracy for RTT measurements, overcoming obstacles associated with AP cooperation and calibration. The proposed methods open avenues for broader implementation of Wi-Fi RTT technology, with implications for diverse applications such as asset tracking and location-based analytics.
As the demand for more accurate and reliable indoor positioning systems continues to grow, Wi-Fi Round-Trip Time (RTT) technology emerges as a promising solution. This Master’s Thesis investigates the potential of Wi-Fi RTT in enhancing location-based services (LBS) within indoor environments. The main aim of this research is the compatibility aspect of RTT with legacy devices.
Wi-Fi’s fine-time measurement (FTM) protocol, designed for indoor localization with high accuracy, relies on cooperative signal round-trip time (RTT) measurements between two devices. However, as of 2023, the adoption of the FTM protocol among commercially-deployed Wi-Fi access points (APs) remains limited. This thesis addresses this limitation by exploring a general solution for positioning by taking also the one-sided RTT measurements into consideration. This technique, which operates independently of AP cooperation, would enable mobile devices to obtain distance measurements with most existing APs.
This thesis covers two positioning methodologies: coverage area ellipse-based method and multilateration. Moreover, the mathematical concepts that make these methodologies possible are explained in detail, including the derivation of multivariate normal distribution and the Gauss-Newton method for minimization.
This research contributes to advancing the field of indoor positioning by providing promising accuracy for RTT measurements, overcoming obstacles associated with AP cooperation and calibration. The proposed methods open avenues for broader implementation of Wi-Fi RTT technology, with implications for diverse applications such as asset tracking and location-based analytics.