Research On Indoor Multi-information Fusion Positioning Technology Based On Smartphone

In recent years,the requirement of indoor location service for humans has become stronger.After the persistent efforts of predecessors,many new positioning technologies have been developed in the field of indoor location.For example,WiFi positioning technology and the technology of using pedestrian dead reckoning(PDR)based on inertial sensors.Compared with other positioning technology,the two location technology has the advantages of easy realization,low cost,good reliability,small external dependence and many other advantages,and can use smartphone as positioning platform.WiFi positioning technology has no cumulative error but is easily affected by indoor environment,while PDR location technology is not affected by environments.However,there are error accumulation problems of the technology.The two localization technologies have their advantages and disadvantages.To this end,an indoor multi-information fusion location scheme that using kalman filter algorithm to fuse the two positioning methods to improve the positioning accuracy in this paper is presented,and verified by some experiments.Research works in this paper are summarized as follows.(1)Through the research and comparative analysis of several WiFi positioning technologies based on smartphone,we chose the fingerprint positioning technology based on RSSI as the positioning method of WiFi positioning,and selected the MAC address and received signal strength indicator(RSSI)of WiFi hotspots as the main part of the fingerprint database.In this part,we introduced the nearest neighbor algorithm,K nearest neighbor algorithm and weighted K nearest neighbor algorithm to introduce and analyze three common matching algorithms,and choose the nearest neighbor algorithm which is easy to implement and small in computation as the matching algorithm of the online positioning phase of WiFi positioning technology.(2)The K-means clustering algorithm is studied and the algorithm is used to divide the fingerprint database to achieve the purpose of reducing the amount of matching operations during location correspondence to improve the positioning efficiency.The K-means clustering algorithm is experimented and that is used to match data has better positioning performance and can effectively reduce the time-consuming and maximum error of single point positioning than not using the algorithm.And it makes up for the defects of the original positioning technology.(3)Pedestrian dead reckoning(PDR)location technology is mainly researched fromthree key factors of gait detection,the estimation of the step length and the obtaining of course angle.Based on the analysis of human's gait and related experiments on the aspect of gait detection,the acceleration of three axes of acceleration sensor is selected as the basic data and peak detection method is used to recognize the pedestrians stepped.In the aspect of the step length estimation,a new step length model based on the interval time of every step is put forward.The course angle is measured and then filtered by the direction sensor.And the practicability of the model is verified by experiments.In order to eliminate the cumulative error of PDR technology,the two-dimensional code correction is innovatively introduced to the algorithm,and the implementations of coding and decoding are provided.The experiment is verified in the experiment.(4)Based on the research of improved WiFi positioning technology and PDR technology,the solution of using kalman filter algorithm for data fusion is proposed on the combination of characteristics of two kinds of positioning technology.And the experimental results show that the fusion solution has obvious advantages in average positioning error and error distribution,and the solution can satisfy the accuracy requests of indoor positioning.