Indoor Positioning Using Machine Learning With Wi-Fi FTM and RSSI

Indoor positioning, aimed at monitoring human behavior, overseeing IoT device operations, and self-localization of indoor mobile entities, has garnered significant attention. One of the positioning methods introduced to the market is the IEEE 802.11 FTM (Fine Time Measurement) protocol, which estimates indoor location by measuring the distance based on the propagation time between Wi-Fi Access Points (AP) and stations (STA). In this paper, we present an indoor positioning method by measuring FTM and RSSI in an indoor environment, and apply machine learning to the collected data. In actual indoor environments, it is conceivable that obstacles may alter or block the radio propagation paths, affecting the propagation time of FTM and RSSI used in machine learning, thereby impacting the accuracy of position estimation. We created scenarios with and without obstacles that block radio propagation indoors, collected data, and performed position estimation using supervised machine learning. Our results indicate that if the input data includes at least one FTM-enabled Wi-Fi AP, the accuracy of position estimation using machine learning improves compared to using only RSSI. Additionally, the results showcase that the proposed method is effective for position estimation even in environments with indoor obstacles.