TWPad: Through the wall passive human detection based on joint hypothesis statistical test

Wi-Fi based passive human detection has attracted numerous research interests recently. For the real-world application, however, the human detection under the through-the-wall (TTW) scenario needs to be addressed. In this paper, we consider the signal spatial distribution from a statistical perspective and propose TWPad, a unified scheme for TTW stationary and moving human detection based on Wi-Fi channel state information (CSI). Specifically, TWPad first extracts the angle of arrival (AoA) of the multipath signals under the TTW scenario and conduct the Jarque-Bera (JB) test on AoA to analyze the normality of the signal spatial distribution. Then, a novel joint Mann-Whitney U (for non-normal distribution) and T-test (for normal distribution) hypothesis test algorithm is proposed to monitor changes in signal spatial distribution. By doing this, TWPad can capture the disturbance in the spatial distribution of multipath signals caused by the moving or stationary human and realize detection under the TTW scenario. The experimental evaluation shows that the TWPad’s F1-measure of stationary human detection can reach about 0.975 and 0.967, under the TTW scenario of glass and brick wall, respectively, outperforming the state-of-the-art solutions and shedding promising lights on ubiquitous human detection in practice.