Research On Fusion Positioning Method Of WiFi And Multi-sensor

Recent years,more and more researchers have combined multiple psitioning technologies to overcome the limitations brought by single technology and cope with complicated and changeable environments,improving the positioning accuracy.Among them,the fusion method of WiFi technology and inertial technology has been widely studied due to the universality of their infrastructures.The performance for the fusion system of WiFi technology and inertial technology is determined by the performance of the two methods.Due to the indoor positioning system is finally facing the market,the main research direction of indoor pedestrian navigation system has shifted from the original single positioning accuracy to how ensure the positioning accuracy while reducing the cost of the system and improving the online response performance.Based on the research and analysis of a large number of fusion positioning methods,this paper starts with WiFi positioning and inertial sensor positioning,and finally achieves the purpose of optimizing the fusion method.The main research and work of the paper are as follows:(1)In the separate WiFi positioning technology,a two-stage indoor fingerprint positioning method based on both WiFi signals is proposed to address the problems of low accuracy when positioning by RSS and long time when positioning by CSI.Considering the advantages and disadvantages of using two kinds of signals for positioning separately in WiFi technology,the method fully utilize advantage of fastspeed of RSS positioning method and high accuracy of CSI positioning method.firstly,RSS and MDS algorithm are used for coarse positioning and subfingerprint library is constructed.Due to the high cost of CSI data in the computation,the redundancy of CSI data is subsequently reduced by linear time-domain filtering,finally use CSI signals to accuracy localization from the newly constructed sub-fingerprint database.While ensuring the positioning accuracy,the response performance of the system is improved.(2)A map-assisted algorithm based on improved particle filtering is proposed for the situation where there is no WiFi signal in the room or the WiFi signal is too weak to be used and the cumulative error is too large when using inertial positioning system to achieve positioning.In order to reduce the gait detection error of INS module,an adaptive high and low threshold method is used to reduce the number of gait detection errors in the variable speed walking state.The introduction of a cascade structure to reduce the update frequency of the upper layer of particle filtering,reduce overhead,and finally face the problem of particle filtering update process particle scarcity,the use of improved firefly algorithm in the measurement of the update stage to change the state of particles through the wall,improve the performance of particle filtering,and finally achieve the purpose of improving positioning accuracy.(3)A 3D positioning method based on WiFi and inertial sensors is proposed to address the problems of high cost in the offline phase,poor scalability and online response of the previous fusion positioning method based on the KNN model,as well as the 3D positioning needs in multi-story buildings.By introducing the MDS model,the scalability of the WiFi and multi-sensor fusion positioning system is improved when achieving large area positioning,and the response performance of the system is improved while ensuring positioning accuracy.The proposed combined WiFi and multi-sensor height estimation method enables continuous and stable height measurements without additional deployment facilities(e.g.,cameras).