Study On The Indoor Positioning Algorithms Based On WiFi And Bluetooth Fusion

With the popularity of handheld devices,such as cellphones,tablets,laptops and wearable devices,equipment of Internet of things has a quick development in performance.People begin to pay more attention to the location-awareness technology.The need and application of location-awareness technology is increasing fast.Nowadays,this technology is extending from outdoor to indoor.In outdoor environment,GNSS is able to give us location and navigation service with high precision.In indoor environment,signal of satellites is obstructed by the building,so indoor environment can not be real-time location via satellite system.This makes us to find out other methods using wireless signal to serve location and navigation.This paper focuses on the WiFi and Bluetooth fusion methods to solve indoor positioning.This paper expounds an adaptive weight distribution WiFi-Bluetooth fusion algorithm based on distance constraint and a fusion algorithm based on Bayesian estimation.The main work and contributions are summarized as follows:When using WiFI positioning,The fingerprint points with a triangle structure layout is designed for acquiring the WiFi signal information,which aims at long and narrow property of corridor space.Dynamic and static positioning experiments determine a optimal clustering parameter for fingerprint clustering process.A dual cluster match method for improving the accuracy and reducing the time for cluster matching is proposed,using the addresses information between real-time and fingerprint for coarse matching to determine a scope of clusters,then using the distance difference of RSS between them to determine the only cluster.Then,use WKNN to calculate user's position.The experiment shows that WKNN can get a higher precision than normal KNN.When using Bluetooth positioning,we choose Logarithmic distance ranging model from the two introduced models.Gather Bluetooth signal strength at reference points,the distances between Bluetooth node and the points are known.With this information,we use Least Square Method to fit the function between distance and received signal strength.There are some errors in each step,so we cannot get the position directly.This paper proposes a triangulation positioning method based on Gaussian distribution,It is proved by experiment that this method can get a high precision positioning result.This paper aims at fusing this two technologies' advantages to get a higher precision,more steady positioning system.Kalman Filter is used to optimize received signal strength.Two fusion methods are proposed: an adaptive weight distribution WiFi-Bluetooth fusion algorithm based on distance constraint and an WiFi-Bluetooth fusion algorithm based on Bayesian estimation.The former algorithm is used in dynamic positioning and the latter is used in static positioning.Experiment result shows that the two methods both can get a higher precision position result and more steady position effect.At last,the work of this paper is summarized,some defects are explained,and gives my opinions about the prophet of WiFI and Bluetooth positioning.