Research On Indoor Positioning Based On WiFi Location Fingerprint

With the rapid development of communication technology,Location Based Services(LBS)have been widely used in many fields.The Global Navigation Satellite System(GNSS)provides all-weather positioning services for military and civilian use.Due to the shielding effect of buildings on radio signals,GNSS cannot provide accurate location services indoors,especially in places such as underground wells,tunnels,and underground shopping malls.Therefore,it is necessary to study indoor positioning.The popularity of WiFi has promoted the development of indoor positioning.While indoor WiFi Access Points(AP)provide Internet access services for terminal devices,the Received Signal Strength Indicator(RSSI),which is used to determine link quality,has the function of identifying locations.This paper studies indoor positioning based on WiFi location fingerprints,aiming to further improve the accuracy and real-time performance of indoor positioning on the basis of existing research.The specific research work of this paper is as follows:(1)Research the indoor positioning accuracy improvement algorithm.This paper analyzes the characteristics of RSSI,focusing on the time characteristics,distance characteristics and distribution characteristics of RSSI to provide guidance for the preprocessing of RSSI.Then this paper analyzes the influence of selecting different features of RSSI as the location fingerprint on the positioning error.Based on the above research,this paper proposes a fingerprint database preprocessing method based on image Gaussian filtering,which reduces the noise of the fingerprint database and improves the accuracy of the fingerprint database.In order to solve the problem that the number of nearest neighbors K is difficult to choose in the Weighted K-Nearest Neighbor(WKNN)positioning algorithm,this paper proposes an improved adaptive WKNN algorithm based on the K-Means clustering algorithm.The experimental results show that the algorithm improves the positioning accuracy.(2)Research the algorithm to improve the real-time performance of indoor positioning.Aiming at the problem of poor real-time performance in the online positioning stage of largescale fingerprint database,this paper proposes an improved WKNN algorithm based on Locality Sensitive Hashing(LSH).This algorithm effectively narrows the search range of the fingerprint database during positioning,thereby shortening the time of online positioning and improving the real-time performance of positioning.(3)Develop a portable indoor positioning system.This paper develops an indoor positioning system based on the Client / Server(C/S)architecture,the client is based on the Android platform,and the server is based on the Spring Boot framework.The client is responsible for sending a positioning request to the server,the server is responsible for solving the position and returning the result to the client,and the client displays the position on the map.The final test experiments in this paper show that the system has good stability and practicability in indoor positioning scenarios.