Leveraging Hypothesis Testing for CSI Based Passive Human Intrusion Direction Detection

Due to the pervasive deployment of Wi-Fi devices, Wi-Fi-based human intrusion detection has attracted intensive attention in recent years. By analyzing temporal changes of signal caused by human movement, the existing solutions can achieve passive human intrusion detection. However, the detection of intrusion direction, from which the person intrudes the area of interests, is still a challenging task. Considering the influence of intrusion direction on the energy and spatial statistical distributions of multiple reflections, in this study, we propose WIDD, a Wi-Fi-based passive human intrusion direction detection system. Concretely, WIDD first extracts the angle of arrival (AoA) and amplitude distributions of the multipath signal and conducts the Jarque-Bera (JB) test to analyze the normality of the obtained distributions. A novel joint hypothesis testing algorithm is proposed to monitor changes in amplitude and AoA distributions of reflections and realize the human intrusion detection based on the analysis result. At last, it finds the reflection affected by the human intrusion most via the divergence evaluation and extracts the corresponding AoA to realize the intrusion direction detection. The comprehensive experimental evaluation demonstrates that WIDD provides human intrusion direction detection accuracy of about 0.943 and 0.931, under the scenario where the transmitter and receiver are separated by the glass and brick wall, respectively, outperforming the state-of-the-art solutions and shedding bright lights on ubiquitous human detection in practice.

Automated summary

By analyzing the multipath signal data and conducting hypothesis testing on Wi-Fi signals, the proposed system provides an accurate passive human intrusion direction detection, which outperforms existing solutions and shows potential for practical applications in human detection.