The Research Of Identification Algorithm In Indoor Illegal Intrusion Detection Based On Wi-Fi Technique

Indoor safety relates to each person’s life and property, thus it has been the focus of public for a long time. Researchers have proposed lots of strategies to solve this problem, like video surveillance and infrared monitoring. However, these strategies are depending on additional special devices, for example, camera and infrared generator. Such additional device costs limited their promotion, and we need a lower overhead and reliable solution.With the popularity of smart devices, Wi-Fi technique has increasingly favored by the public. Wi-Fi could be found everywhere, from shopping mall, office to home. Thus various algorithms and systems are proposed like gesture identification and sleeping monitoring, etc. Among them, some researchers have focused on the human detection area and proposed related identification algorithm. However, these algorithms could only judge whether someone bursts into the interested region, they are unable to identify the user or intruder.Ideally, the algorithm could automatically detect the intruder and users’ participation. Based on this target, we proposed an intruder detection algorithm based on Wi-Fi, which could effectively identify intruder in real time. The theory basis is different user has disparate living habit, which can be interpreted as when and where, what action does the user do. Time can be acquired by time stamp, and place can be acquired by summarizing the signal strength attenuation with distance. Because user’s action could cause signal fluctuation, and fluctuation caused by the same action is similar. Thus through learning and training of the signal fluctuation, we would deduce the related actions.So far, we can build tailored behavior habit model for special user. The whole algorithm is divided into four modules, those are signal preprocessing module, model building module, intruder detection module and alert confirmation module, and each module has its own goal. The first signal-preprocessing module aims to filter the noise by principle component analysis as well as segment the time continuous signal sequence by sliding window and bottom-up technique. According to the precondition of that each user have different behavior habitat, the second model-building module mainly builds tailored behavior habit model by leveraging hidden markov model. The third intruder detection module identifies illegal intruder through threshold detection, and the fourth alert confirmation module make alert confirmation used gesture identification. The experiments verify the effectiveness of the proposed algorithm, and the detection precision can reach 93.4%, demonstrating that the algorithm could reduce user’s property loss and achieved desired results.