Abstract:

Indoor positioning methods using wireless signal propagation data have attracted significant interest for applications such as monitoring IoT devices and tracking human behavior. The IEEE 802.11 FTM (Fine Timing Measurement) protocol, used for Wi-Fi location, was introduced to the market in 2016. The FTM protocol can measure the distance between Wi-Fi access point (AP) and station (STA). By measuring the distances between multiple Wi-Fi APs and STAs, the indoor position of the STA can be estimated. In this paper, we collected FTM and RSSI measurements in an indoor environment using FTM- enabled Wi-Fi APs and STAs, and evaluated indoor positioning accuracy through geometric calculations based on FTM data as well as a machine learning approach using both FTM and RSSI data as inputs. Furthermore, for the machine learning approach, we also assessed the impact of varying the number of Wi-Fi AP data elements supplied to the model in increments of AP count. The results demonstrated that positioning accuracy achieved by the machine learning approach surpassed that of geometric calculations. Moreover, even when the number of input data elements to the machine learning model was limited, utilizing FTM data obtained from at least one AP mitigated the degradation in positioning accuracy within the machine learning framework.