AP-centred Enterprise Wi-Fi Indoor Location System Research And Deployment

With the development of wireless network technology and the popularization of mobile terminals,various mobile Internet applications have mushroomed into our daily life.In order to meet the increasing demand of location-based services,many researchers are committed to provide more accurate location estimation in different environments.More and more wireless technologies have been applied to the research of indoor location technology.Among them,the use of Wi-Fi to achieve indoor positioning system has became a research hotspot in the field of indoor positioning due to its advantages such as simple implementation,low deployment cost and strong scalability.In this thesis,the research status of Wi-Fi positioning at home and abroad has been deeply studied and analyzed.Aiming at the application scenarios of enterprise-level deployment,an AP-centered Wi-Fi indoor positioning system is proposed.The system makes full use of the existing enterprise Wi-Fi network architecture,no additional hardware equipment needed,low deployment cost.The positioning process requires no Wi-Fi connection and no specific software installation,and integrates multi-target sensing positioning and user active positioning.The research in this thesis is a positive exploration of the application of Wi-Fi indoor positioning technology.The main work of the thesis is as follows:(1)In this thesis,two signal characteristics commonly used in Wi-Fi location,namely RSSI and CSI,were analyzed in depth from the perspective of wireless communication theory and IEEE 802.11 protocol,and Wi-Fi signal characteristics were extracted and compared through experiments.Finally,we chose to sniff and parse the Probe Request frame from the AP side as the positioning data source of the system.(2)This thesis improves the traditional fingerprint algorithm for enterprise-level deployment application scenarios,and improves its performance in positioning effectiveness and positioning accuracy.Firstly,this thesis establishes the fingerprint database through the subregional table and uses the k-means algorithm to cluster the fingerprint table,which reduces the search space of the fingerprint algorithm and improves the time efficiency of the algorithm.Then,the traditional WKNN algorithm is improved and the AP sequence matching degree is introduced to further eliminate the positioning error caused by the introduction of a far reference point.Finally,a local fingerprint update algorithm based on clustering is proposed to solve the problem of time and energy consumption in large-scale fingerprint update process.(3)Combined with the theoretical analysis and positioning algorithm of this thesis,this thesis has realized the indoor positioning system centering on AP and deployed it in some areas of the 2nd floor of block B of the scientific research building of the school.Finally,the results of multi-target sensing positioning and user active positioning are verified by system experiments.