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Device-free passive (Dfp) motion detection seeks to monitor the position change of entities without actively carrying any physical devices. Recently, WLAN with a rich set of installed wireless infrastructures enables motion detection in the area of interest. WLAN-enabled DfP motion detection rely on received signal strength (RSS) is verified to be able to provide acceptable high accuracy. Although RSS can be easily measured with commercial equipments, it is suspectable to measurement itself due to multipath effect in indoor environment. In this paper, we present an Indoor device-free Motion Detection system (FIMD) to overcome the preceding RSS-based limitation. FIMD explores properties of Channel State Information (CSI) from PHY layer in OFDM system. FIMD is designed based on the insight that CSI maintains temporal stability in static environment, while exhibits burst patterns when motion takes place. Motivated by this observation, FIMD uses a novel feature extracted from CSI to leverage its temporal stability and frequency diversity. The motion detection is conducted with outliers identification from normal features in continuous monitoring using density-based DBSCAN algorithm. Moreover, we leverage two schemes including false alert filter and data fusion to enhance the detection accuracy. We implement FIMD system with commercial IEEE 802.11n NICs and evaluate its performance in two typical indoor scenarios. Experiment results show that FIMD can achieve high detection rate. Moreover, comparing with RSSI, the feature extracted from CSI enables better detection performance in accuracy and robustness to narrowband interference.
Xiao et al. (Sat,) studied this question.