Develop an Adaptive Real-Time Indoor Intrusion Detection System Based on Empirical Analysis of OFDM Subcarriers

Device-free passive intrusion detection is a promising technology to determine whether moving subjects are present without deploying any specific sensors or devices in the area of interest. With the rapid development of wireless technology, multi-input multi-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) which were originally exploited to improve the stability and bandwidth of Wi-Fi communication, can now support extensive applications such as indoor intrusion detection, patient monitoring, and healthcare monitoring for the elderly. At present, most research works use channel state information (CSI) in the IEEE 802.11n standard to analyze signals and select features. However, there are very limited studies on intrusion detection in real home environments that consider scenarios that include different motion speeds, different numbers of intruders, varying locations of devices, and whether people are present sleeping at home. In this paper, we propose an adaptive real-time indoor intrusion detection system using subcarrier correlation-based features based on the characteristics of narrow frequency spacing of adjacent subcarriers. We propose a link-pair selection algorithm for choosing an optimal link pair as a baseline for subsequent CSI processing. We prototype our system on commercial Wi-Fi devices and compare the overall performance with those of state-of-the-art approaches. The experimental results demonstrate that our system achieves impressive performance regardless of intruder's motion speeds, number of intruders, non-line-of-sight conditions, and sleeping occupant conditions.