Design And Implementation Of Indoor Location Algorithm Based On Multiple MEMS Sensors Data Fusion

With the development of the Internet of Things,the demand for indoor positioning and navigation technology is increasing.Due to the block of indoor signal,it is infeasible to adopt traditional technology like GPS to determine a certain position.Therefore,indoor technology has gradually become the focus of current research,and mainly includes location technology based on all kinds of wireless as well as indoor positioning technology based on inertial sensor.The thesis is about the study of indoor pedestrian orientation on the basis of inertial sensor,using inertial sensor on the embedded system platform to realize the algorithm.The indoor positioning system in this thesis is based on MEMS sensor,using inertial measurement unit,calculating location information through Pedestrian Dead Recknoning,and correcting with the particle filter.This thesis introduces and analyzes the traditional sensor based on MEMS inertial positioning scheme as well as the existing problems,and brings up with the method of data fusion in combination with gyroscope,accelerometer and magnetometer data to improve navigation calculation accuracy and robustness.Moreover,this thesis designs a kind of particle filter algorithm for the indoor floor plan,and puts forward a scheme of adaptive resampling to resolve particle degradation problems occuring in traditional particle filter algorithm.Through this kind of improved particle filter,combining with the indoor floor plan information,it is expected to extract the course and step length information calculated by Pedestrian Dead Recknoning and modify the position information of each step,to achieve the goal of correcting cumulative error.Finally,in this thesis,the test and analysis of modules are carried out by the comprehensive verification experiment.Test results show that the frequency detection module error is less than 1%,and course angle error is controlled within 2°,performing well to magnetic interference.Moreover,the application tests show that the indoor positioning algorithm combined with the pedestrian path inference algorithm and the fusion particle filter is expected to control the indoor positioning system positioning error within 2%,which meet the design index requirements.