Leveraging Polarization of WiFi Signals to Simultaneously Track Multiple People

Abstract

This paper presents WiPolar, an approach that simultaneously tracks multiple people using commodity WiFi devices. While two recent papers have also demonstrated multi-person tracking using commodity devices, they either require the people to continuously keep moving without stopping, and/or require the number of people to be input manually, and/or keep the WiFi devices from performing their primary function of data communication. Motivated by the increasing availability of polarized antennas on modern WiFi devices, WiPolar leverages signal polarization to perform accurate multi-person tracking using commodity devices while addressing the three limitations of prior work mentioned above. The key insight that WiPolar is based on is that different people expose different instantaneous horizontal and vertical radar cross-sections to WiFi transmitters due to differences in their physiques and orientations with respect to the transmitter. This enables WiPolar to accurately separate the multipaths reflected from different people, which, in turn, allows it to track them simultaneously. To the best of our knowledge, this is the first work that leverages polarization of WiFi signals to localize and track people. We implement WiPolar using commodity WiFi devices and extensively evaluate it for tracking up to five people in three different environments. Our results show that WiPolar achieved a median tracking error of just 56cm across all experiments. It also accurately tracks people even when they were not moving. WiPolar achieved a median tracking error of 74cm for people that were either stationary or just taking a small pause.