Yuzuki Yaguchi矢口 悠月（やぐち ゆづき）

Multi-floor indoor positioning with Wi-Fi RTT

GPS-based navigation offers high accuracy outdoors, but its accuracy degrades considerably indoors and underground, where satellite signals are attenuated and reflected. Improving the accuracy of indoor positioning is therefore an urgent problem, and a variety of methods based on wireless communication technologies have been studied. Among them, Wi-Fi based methods are attractive in practice because they require no dedicated hardware and can reuse existing communication infrastructure.

Fingerprinting, an absolute positioning method based on the received signal strength indicator (RSSI), is the mainstream approach for Wi-Fi indoor positioning. It determines the position by matching the RSSI values and the access point identifiers (BSSIDs) observed at positioning time against a database built in advance. While fingerprinting achieves very high accuracy, it is vulnerable to signal instability and external interference, and its performance degrades markedly under signal fluctuation. Wi-Fi RTT, standardized as IEEE 802.11mc in 2016, is a ranging technique that uses the round-trip time of signals between a smartphone and an access point, and has attracted attention for its accuracy.

Most prior work on Wi-Fi RTT assumes a single-floor environment in which the access points and the terminal are placed on the same floor. Real environments, however, are not as simple as a laboratory: multi-floor structures, atrium spaces and dense human-body shadowing are intertwined and unevenly distributed. My work focuses on Wi-Fi RTT positioning in multi-floor environments, where access points and the terminal are separated by floors — a setting more realistic than that of prior work.

Higher accuracy in Wi-Fi RTT positioning would enable stable positioning in complex environments using Wi-Fi infrastructure alone, contributing to a universal navigation infrastructure for public spaces. In particular, it is directly relevant to improving accessibility and providing real-time location information in high-rise buildings, airports, railway stations and commercial facilities.