LAUSR

Some pioneer WiFi-based passive human tracking systems have achieved submeter-level accuracy. However, their key limitation is that the location of WiFi devices must be known in advance. To this end, we propose WiSen, a novel system that estimates the location of a passive user in an indoor environment without any prior knowledge about the location of WiFi devices. Specifically, we first exploit a signal power model for human-induced reflection signal extraction and multidimensional parameter estimation. Due to measurement noise and low-resolution parameter estimates, we further design a confidence-aware-based path pruning (PP) method that combines the distribution of path parameters from successive windows to select reliable human-induced reflection paths. Before that, we also adopt a novel data augmentation (DA) method to increase the number of available path parameters for better learning parameter distribution. These path parameters after PP are then used to statistically estimate the relative location of the transmitter with respect to the receiver and finally localize the passive user. We implement WiSen on commodity WiFi devices and validate its performance in real indoor environments. The experimental results show that WiSen can realize the submeter-level accuracy for passive human tracking and device localization with only a single WiFi link.