Experimentations and Analysis on Indoor Positioning through Fusion with Inertial Sensors and Dynamically Calibrated Wi-Fi FTM Ranging

Abstract

By fusing the relative inertial sensory data and Wi-Fi Fine Time Measurement (FTM) ranging which can be utilized to calculate absolute position through Extended Kalman Filter (EKF) fusion framework with an extra dynamical Wi-Fi FTM ranging calibration mechanism, the indoor positioning performance was evaluated. The result shows that the positioning error reaches down to 1.5 m in some certain environmental configurations. Analytical findings were retrieved from experimental setups that resemble various typical indoor scenarios. It shows promising result utilizing the widely applied inertial sensory algorithm such as Pedestrian Dead Reckoning (PDR), together with the Wi-Fi FTM capable access points.