The Design Of Integrated Navigation Method For Indoor INS/UWB Pedestrian

With the advent of the information age in the 21 st century,the range of people's work and life has been expanding.The emergence and development of Global Positioning System(GPS)has greatly promoted the progress of human industrial production and life.However,GPS signals are easily occluded by objects in complex environments,especially in indoor environments.The accuracy of GPS positioning decreases rapidly,and even loss of lock occurs.In order to meet the growing demand for high-precision indoor location information,indoor positioning has gradually become a research hotspot.This dissertation studies the indoor pedestrian location problem under the complex indoor environment.Firstly,the performance of several common indoor navigation technologies is analyzed.It can be seen from the analysis that there are some shortcomings in the single navigation technology,which is difficult to meet the continuous high-precision positioning demand.In order to overcome the above shortcomings,an Ultra Wide Band(UWB)/Inertial Navigation System(INS)integrated positioning method are proposed in this paper,which makes full use of the advantages of UWB anti-jamming and penetration ability and INS self-navigation ability to obtain continuous and stable navigation information.Based on INS/UWB integrated positioning system,this paper studies the loose and tight combined positioning methods.For the loosely combined location method,the difference between the position of the target pedestrian measured by INS and UWB is used as the observation vector,and then the data are processed by Kalman filter.Although the loosely combined positioning method is easy to implement,it is easy to introduce the position error of sub-positioning system in the process of data fusion.In order to solve this problem,this paper studies the INS/UWB tightly combined positioning method.In this method,the distance difference between the target pedestrian and the reference node measured by INS and UWB technology is used as the observation vector,and the pedestrian position is predicted and estimated by Kalman filter.It should be pointed out that although the above methods can improve the positioning accuracy,Kalman filter prediction and estimation depends on the accuracy of noise statistical characteristics,but the accuracy of noise statistical characteristics is difficult to obtain accurately in practical application environment.In order to improve the robustness of data fusion algorithm,a finite impulse response(FIR)filter is proposed,and which is used in INS/UWB loosely and tightly combined positioning data fusion algorithm.The positioning accuracy of FIR filter is only affected by the length of filtering window under certain conditions.In order to improve the positioning accuracy and select the optimal filter window length at the current time,an adaptive FIR filter is proposed.The optimal filter window length at the current time is obtained by calculating the Mahalanobis distance.At the same time,in order to verify the performance of the above algorithm,this paper builds an INS/UWB integrated positioning experimental platform,and develops the upper computer software to collect and save the experimental data.The semi-physical simulation results show that the positioning accuracy of tight combination is better than that of loose combination,the robustness of FIR filter is better than that of Kalman filter,and the adaptive FIR filter algorithm can effectively improve the positioning accuracy in practical environment.