Research On Passive Indoor Human Localization Techniques Based On WiFi

With the popularization and development of Io T technology,ubiquitous wireless sensing has played an important role in many fields such as smart healthcare,smart home,security monitoring,and intrusion detection.The realization of passive indoor personnel positioning function is the basis for applying ubiquitous wireless sensing system,and existing passive indoor positioning technologies mainly include technologies based on Wi Fi,IR-UWB,and sound waves.Among many positioning technologies,due to the widespread deployment of Wi Fi devices in various scenarios of production and life,Wi Fi-based passive personnel positioning technology has received extensive attention from both industry and academia in recent years.Currently,Wi Fi-based passive indoor positioning technology mainly relies on extracting channel state information from commercial Wi Fi devices.As human movement can cause corresponding changes in Wi Fi signals,passive indoor personnel positioning can be achieved by analyzing the channel state information and extracting corresponding human motion characteristics.Although numerous studies have demonstrated that Wi Fi has great potential as a sensing technology,in real life,commercial Wi Fi signals are easily affected by the surrounding environment,resulting in existing Wi Fi-based passive indoor positioning work being unable to accurately separate target personnel dynamic components from complex received signals,thereby leading to low positioning accuracy of the existing system.In response to these problems,the research focus of this thesis are as follows:1.A Wi Fi-based passive personnel gait velocity detection system is proposed,which accurately estimates the target’s movement velocity and provides upstream data information for downstream work such as indoor positioning and action recognition in the wireless sensing field.The system first estimates the quality of subcarriers at different frequencies through a subcarrier selection algorithm that selects high-quality subcarriers for parameter estimation.Then,by using a Doppler frequency shift estimation algorithm based on the observation matrix to extract the Doppler frequency offset of moving targets from the signal with multipath interference,the accurate signal arrival angle is extracted from the redundant environmental noise by the time and space dual-window signal arrival angle estimation method.Finally,the target personnel’s gait velocity is accurately calculated through a gait velocity estimation algorithm.Through a large number of experiments,the effectiveness and robustness of the system are effectively verified.This system solves the problem of large differences in subcarrier sensing granularity,effectively reduces errors in Doppler frequency shift estimation and signal arrival angle estimation,and improves the accuracy of gait velocity estimation.2.A Wi Fi-based passive indoor personnel positioning system is proposed,which further locates the target personnel’s movement trajectory based on the target personnel’s movement velocity information estimated by the Wi Fi-based passive personnel gait velocity detection system.The system first models the channel state information extracted from commercial Wi Fi devices;then,through a ranging algorithm,the signal propagation path length is extracted from the Doppler frequency offset of the received signal,and the current position of the target is estimated based on the estimated signal arrival angle;finally,the trajectory fitting algorithm is used to further improve the positioning accuracy of the positioning system.This thesis conducts a large number of experiments and comparative experiments on various motion trajectories using the typical Wi Fi-based passive indoor positioning technology Widar2.0 public dataset.The experimental results show that the median error of the proposed indoor positioning method for indoor personnel motion trajectory positioning is 80 cm,which improves the positioning accuracy by 25.9%compared to the existing typical Widar2.0 positioning accuracy and effectively demonstrates the feasibility and robustness of the system.