Leveraging TPDV to Direction Estimation With a Single Pair of Commodity Devices

Jing Nan,Guo Wei,Wang Lin,Yang Yuling

doi:10.1109/access.2020.2984509

Abstract

In this paper, we propose an indoor passive human tracking system equipped with only one pair of commodity Wi-Fi devices, namely WiBlinker. The key insight is that the motion of moving target not only introduce Doppler frequency shift, but also changes the value of phase difference between two adjacent antennas of the receiver. First of all, a light-weight interpolation scheme is presented to remove the random phase noise in channel state information for improving the accuracy of the trajectory tracing. Instead of using AoA to obtain the moving direction, WiBlinker employs the trend of phase difference variation in direction estimation which is no need multiple Wi-Fi devices and easier to be achieved. Experimental results show that WiBlinker outperforms the conventional multiple Wi-Fi links methods, especially in the case of a sharp turn.

Highlights

Indoor human tracking is playing a key role in many emerging applications, such as security surveillance, behavioral analysis, patient care, indoor navigation, etc
In contrast to that the fine-grained tracking methods based on Arrival of Angle (AoA) need more parameters as an assistant to achieve tracing, only Trend of Phase Difference Variation (TPDV) and Doppler frequency shift (DFS) of the Wi-Fi signals reflected from the human body are employed to accurately estimate the moving direction and track the moving human
In order to fully eliminate the phase offset without introducing new phase errors, we propose a Dynamic and Light-Weight Interpolation Elimination Method (DL-IEM) to dynamically insert specified number number of subcarriers according to the SNR of subcarriers

Summary

INTRODUCTION

Indoor human tracking is playing a key role in many emerging applications, such as security surveillance, behavioral analysis, patient care, indoor navigation, etc. In contrast to that the fine-grained tracking methods based on AoA need more parameters as an assistant to achieve tracing, only Trend of Phase Difference Variation (TPDV) and DFS of the Wi-Fi signals reflected from the human body are employed to accurately estimate the moving direction and track the moving human. It is difficult to precisely estimate the direction of the moving target only with AoA due to the limitation on the number of commercial Wi-Fi antennas To address this problem, WiDir [21] analyzes the phase change dynamics from multiple Wi-Fi subcarriers based on the Fresnel zone (FZ) model to infer the walking direction. (3) We implement WiBlinker with only one single pair of Wi-Fi devices without any extra hardware modification, by which we achieve precise direction estimation and trajectory track.

PHASE ERROR

DIRECTION ESTIMATION MODEL

SYSTEM PERFORMANCE

VIII. CONCLUSION