WiDetect: Robust Motion Detection with a Statistical Electromagnetic Model

Motion detection acts as a key component for a range of applications such as home security, occupancy and activity monitoring, retail analytics, etc. Most existing solutions, however, require special installation and calibration and suffer from frequent false alarms with very limited coverage. In this paper, we propose WiDetect, a highly accurate, robust, and calibration-free wireless motion detector that achieves almost zero false alarm rate and large through-the-wall coverage. Different from previous approaches that either extract data-driven features or assume a few reflection multipaths, we model the problem from a perspective of statistical electromagnetic (EM) by accounting for all multipaths indoors. By exploiting the statistical theory of EM waves, we establish a connection between the autocorrelation function of the physical layer channel state information (CSI) and target motion in the environment. On this basis, we devise a novel motion statistic that is independent of environment, location, orientation, and subjects, and then perform a hypothesis testing for motion detection. By harnessing abundant multipaths indoors, WiDetect can detect arbitrary motion, be it in Line-Of-Sight vicinity or behind multiple walls, providing sufficient whole-home coverage for typical apartments and houses using a single link on commodity WiFi. We conduct extensive experiments in a typical office, an apartment, and a single house with different users for an overall period of more than 5 weeks. The results show that WiDetect achieves a remarkable detection accuracy of 99.68% with a zero false rate, significantly outperforming the state-of-the-art solutions and setting up the stage for ubiquitous motion sensing in practice.