Tracking from One Side - Multi-Person Passive Tracking with WiFi Magnitude Measurements

In this paper, we are interested in passively tracking multiple people walking in an area, using only the magnitude of WiFi signals from one WiFi transmitter and a small number of receivers (configured as an array) located on one side of the area. Past works on RF-based tracking either track only a single moving person, use a large number of transceivers surrounding the area to track multiple people, or use additional resources like ultra-wideband signals. Furthermore, magnitude-based tracking provides an attractive feature that additional receiver antennas can easily be added to the antenna array as needed, without the need for phase synchronization, since the magnitude can be measured independently on the different antennas. In this paper, we then propose a new framework that uses only the magnitude of WiFi signals and expresses it in terms of the angles of arrival of signal paths at the receivers as well as the motion parameters of the virtual arrays emulated by the moving people. We then use a two-dimensional MUltiple SIgnal Classification (MUSIC) algorithm to estimate the aforementioned parameters, and further utilize a Particle Filter with a Joint Probabilistic Data Association Filter to track multiple people walking in the area. We extensively validate our proposed framework in both indoor and outdoor areas, through 40 experiments of tracking 1 to 3 people, using only one transmit antenna and three laptops as receivers (a total of four off-the-shelf Intel 5300 WiFi Network Interface Cards (NICs)). Our results show highly accurate tracking (mean error of 38 cm in outdoor areas/closed parking lots, and 55 cm in indoor areas) using minimal WiFi resources on only one side of the area.