Geomagnetic Indoor Positioning Based On Particle Filter And PDR Algorithm

Although progress has been observed in global navigation satellite systems and these systems are widely used,they cannot provide effective navigation and positioning services in signal coverage areas and areas lacking strong signals(such as indoor environments).Therefore,in recent years,indoor positioning technology has become the focus of research and development.The earth's magnetic field is very stable in an open environment,and due to differences in buildings and internal structures,this type of three-dimensional vector magnetic field can be widely used indoors for indoor positioning.In this study,In this study,a smartphone magnetometer was used to collect magnetic field data to construct an indoor magnetic field map.In addition,acceleration sensors and gyroscopes are used to estimate the position change of the mobile phone and detect the number of steps the user walks on the mobile phone.This study designed a program to measure the user's step size,and all the obtained information was input into a pedestrian dead reckoning(PDR)algorithm to calculate the position of the pedestrian target.The improved particle filtering algorithm and the magnetic gradient of the magnetic field map optimize the indoor positioning accuracy of the PDR algorithm.This method combines PDR with geomagnetic positioning by using particle filtering algorithm as the main positioning framework.In order to improve the geomagnetic positioning accuracy and the specificity of geomagnetic fingerprints,a geomagnetic multi-feature positioning algorithm was designed.By extracting three geomagnetic features from the magnetic field data as a single-point fingerprint;then,the method of particle mutation and reconstruction of particle sets was used to improve the problem of particle degradation in the particle filtering algorithm of fusion positioning.At the same time,the acceleration in the PDR algorithm is used to obtain the initial position in the fusion positioning algorithm,which solves the problem that the location of the starting point cannot be determined when using a single PDR algorithm for indoor positioning.Experiments in 10 regions were conducted to evaluate the performance of the proposed method.Experimental results show that the positioning accuracy of the fusion algorithm is increased by an average of 28.73% compared to the single geomagnetic positioning accuracy,and the highest accuracy is increased by 60.06%;the indoor positioning accuracy is improved by an average of 23.94%,and the highest accuracy is increased by 56.60%.The goal of this paper is to solve the problem of complicated and expensive positioning devices by designing a method that can use smartphones to perform indoor positioning,obtain the user's position based on geomagnetic fingerprints by using the mobile sensing information determined by the gyroscope and accelerometer observation value,combined with particle filtering method,calculates the weight for each particle to correct the predicted position.This algorithm effectively improves the accuracy and efficiency of indoor positioning and achieves the goal of this paper.