Research On Passive Indoor Intrusion Detection And Location Technology Based On Wi-Fi

Outdoor GPS navigation technology is now very mature,providing convenience for people's daily outdoor travel.But in cities,when people enter underground passages or commercial buildings,satellite signals can not provide accurate positioning and addressing services because of the characteristic that they can not penetrate the building,so it is necessary to find new communication signals to make up for this deficiency.With the rapid development of communication technology,Wi-Fi has become an indispensable part of people's indoor activities,which takes a great proportion of people's lives.Therefore,indoor intrusion detection and location technology based on Wi-Fi has become a hot research topic.However,affected by the environment,the detection rate of the existing indoor intrusion detection methods is low.There are also some problems such as inaccurate location and huge workload of fingerprint database updating in the indoor location based on fingerprint.At the same time indoor positioning requires that the person being tested should carry specific equipment,such as mobile phones.While when the elderly fall to the ground at home alone,they are unlikely to have any equipment on their body and can not get the equipment in the meantime.In this case,the passive indoor positioning method,which means that the detected person does not need to carry any equipment,is particularly important.In intrusion detection,a passive indoor detection method based on Wi-Fi was proposed in this thesis: FDF-PIHD(Frequency Domain Fingerprint-based Passive Indoor Human Detection).This method is based on channel state information(CSI).After pretreatment,feature fingerprints are generated in frequency domain.Through similarity comparison,it can be judged that the specific state of online scene is no target in room,a stationary target in room or a dynamic target in room.In order to improve the detection accuracy,the simulation experiment combined the voting scheme of three receiving antennas.The simulation results show that the FDF-PIHD detection method can detect more than 90% of these three indoor states,the detection rate of the unmanned scene is 97.5%,the detection rate of the stationary target is 92.6%,and the detection rate of the dynamic target is 98.5%.This method can not only detect whether there is a target in the room,but also further judge the current activity status of the target,which lays a good foundation for the indoor positioning.Traditional fingerprint-based passive indoor positioning was usually using the way to direct positioning.In this thesis a distributed indoor positioning method based on CSI feature fingerprint was proposed.After forming a double fingerprint database,this method judges the approximate area of online target according to the similarity between online fingerprint and rough classification offline fingerprint database,then matching the newly generated fingerprint pair and subdivision offline fingerprint to accomplish the localization.In order to improve the positioning accuracy,the simulation experiment combined the voting scheme of three receiving antennas.Experiments show that the positioning rate of this method is higher than direct positioning,and the positioning rate for static targets is 95.7%,and the positioning rate for dynamic targets is 92.7%.