Two-Phase Positioning System Based on the Fusion of Wi-Fi Signal Strength and Pose Estimation

Abstract

Due to the growing importance of positioning in recent years, global positioning system has become widely used. However, indoor positioning presents significant challenges due to multipath interference, which can cause large signal fluctuations and high positioning errors for signal-based approaches. Recent advances in deep learning have enabled researchers to estimate people's positions and locate them indoors using image recognition technology, but identification of individuals remains challenging. Therefore, we propose a highly accurate indoor positioning approach using Wi-Fi signals and images. This research employs smartphones to measure the signal strength of three Wi-Fi base stations' 2.4 and 5 GHz frequency bands. We then use machine learning techniques to estimate individuals' positions with coarse accuracy. Subsequently, we analyze the positions of individuals in surveillance camera images. We use OpenPose, an open-source multiperson pose estimation system, and propose a foot position extraction algorithm to extract the foot positions of individuals in the image. We obtain the coordinates of the people in the image through direct linear transformation and machine learning. Finally, we match the coordinates obtained from the two techniques to identify the user's device. Experimental results show that the proposed method achieves higher accuracy, with an average error of 0.43 meters.