Stand-off detection of human presence and movement using IEEE 802.11ac beamforming reports

This paper considers the problem of stand-off detection of human presence and movement in indoor environments. We develop a novel approach using IEEE 802.11ac compressed beamforming reports (CBRs). In the proposed system, a sniffer device collects CBRs communicated between devices inside an indoor environment by listening to the IEEE 802.11ac channels. We translate the problem into a multi-class image classification problem. We develop a discrete Fourier transform (DFT) based feature extraction technique to generate input features which vary significantly across different classes of the classification problem. The pattern of these interclass variations of extracted features remains consistent across different indoor environments. The proposed system was trained and tested using measurements from offices, meeting rooms and lecture theatres. It achieved an accuracy higher than 90% even for rooms that were not included as part of the training set.