Passive Human Tracking Using One Pair of Commodity WiFi Devices with Unknown Locations

Some published WiFi-based passive human tracking systems have achieved sub-meter accuracy. However, they require the location of WiFi devices to be known in advance for passive human tracking, which limits their application in practical indoor scenarios. In this paper, we propose WiSen, a novel passive human tracking system using one pair of commodity WiFi devices with unknown locations. First, we introduce a signal power model for human-related signal extraction and multi-dimensional parameter estimation. Due to low-resolution parameter estimates and noise, we further design a confidence-aware-based path pruning method that combines the distribution of path parameters from successive windows to select reliable paths of interest. Before that, we adopt a data augmentation method to increase the number of available paths to learn parameter distributions better. Then, we statistically estimate the transmitter's location using a kernel density estimation method and ultimately yield the user's location using an improved Gaussian Sum filter approach. We validate the performance of WiSen in real-life indoor environments. The experimental results show that WiSen can realize the sub-meter level accuracy for passive human tracking and device localization.