A CSI-Based Indoor Intrusion Detection and Localization Method

Abstract

In this paper, we propose a method for indoor intrusion detection and localization that makes use of channel state information (CSI), which consists of an offline phase and an online phase. In the former, we collect CSI in different scenarios, and at different times, for more comprehensive characterization of signal propagation. To reduce the redundancy and dimensionality of CSI data, we employ the principal component analysis algorithm to extract the main features of CSI, and build the fingerprint database for localization. In the online phase, we first apply the earth mover’s distance algorithm to detect the presence of the person in the test area. Following this, we determine the approximate location of the target according to the change of CSI measurements, and compare this to the fingerprint database, to select reference points to build the sub-fingerprint database. Finally, we evaluate the actual position of this target using the improved k-Nearest Neighbor algorithm.