Transfer Learning of Wi-Fi FTM Responder Positioning with NLOS Identification
Journal
2021 IEEE Topical Conference on Wireless Sensors and Sensor Networks, WiSNeT 2021
Pages
23-26
Date Issued
2021
Author(s)
Abstract
This paper proposes a neural network (NN) model-based method to predict the location of Wi-Fi access points (AP) that support the fine time measurement (FTM) protocol. From the collected FTM data by the robot, the NN is trained on one FTM responder (FTMR) data to recognize non-line-of-sight (NLOS) patterns. Even without knowing the offsets of individual FTMRs in advance, the model can be used to predict the locations of those FTMRs. The knowledge of FTMR location can be further used to understand network density, connectivity and interference characteristics in buildings. Compared with the results of the basic least-squares and circular positioning method, the experimental results increase the positioning accuracy by 86% and 83%, respectively. ? 2021 IEEE.
Subjects
Fine time measurement; Neural network; NLOS identification; Transfer learning; Wi-Fi positioning system
SDGs
Other Subjects
Least squares approximations; Location; Transfer learning; Wireless local area networks (WLAN); Wireless sensor networks; Interference characteristics; Network density; Neural network model; Nlos identifications; Nonline of sight; Positioning accuracy; Positioning methods; Wi-fi access points; Wi-Fi
Type
conference paper