Research On Mobile Target Localization And Tracking Technology Based On UWB

UWB mobile node is affected by the non line of sight(NLOS)environment,which easily leads to low accuracy of location and tracking,poor real-time performance and weak robustness,which can not meet the practical application requirements.this paper studies traditional UWB mobile node positioning and tracking algorithms from two aspects of localization and tracking.The main research is as follows:First,the paper explains the research background and significance of this topic,and introduces the current research status at home and abroad.Next,the existing UWB positioning method,the received signal time of arrival(TOA)method ranging method and the TOA-based positioning algorithm and its performance indicators are introduced in detail.On this basis,the common TOA-based positioning algorithms are simulated and analyzed in the line of sight(LOS)environment and the NLOS environment.Simulation results show least squares(LS)positioning algorithm performs best.In the research of UWB mobile node localization algorithm,firstly analyze the UWB channel impulse response(CIR)signal characteristics and the NLOS error change rule,and give a fuzzy inference positioning algorithm based on CIR signal characteristics to alleviate the NLOS error Method to improve positioning accuracy.On this basis,considering the large deviation between estimated NLOS error and actual NLOS error,this paper proposes a positioning algorithm based on the fusion of fuzzy inference and adaptive anti-NLOS kalman filtering(KF).First use the characteristics of the CIR signal to alleviate the NLOS error in one step,and then use the difference between the innovation and the variance of the innovation in the KF algorithm to adjust the value of innovation,and then alleviate the distance data after one step of mitigation.and improve the ranging accuracy through two steps of mitigation.Finally,the positioning is completed by the LS positioning algorithm.In the case of NLOS,the results of static and dynamic positioning experiments show that the positioning algorithm proposed in this paper has the highest accuracy and effectively improves the low positioning accuracy of UWB mobile nodes.In the research of UWB mobile node tracking algorithm,this paper first analyzes the impact of likelihood probability distribution on particle filtering(PF)algorithm accuracy,and and proposes a particle diversity metric based on stratification.In view of the lack of particle diversity in the traditional resampling PF algorithm,this paper proposes an improved resampling PF algorithm.After each resampling,the resampled particles are judged based on the layered particle diversity metrics.If the diversity is lower than the set threshold g,Gaussian random walk is performed on all particles according to the set variance ?.Enhance the diversity of particles,so as to improve the tracking accuracy of PF algorithm.MATLAB simulation experiment and actual experiment results show that the PF algorithm based on improved resampling proposed in this paper has the highest tracking accuracy and effectively improves the effect of nonlinear target tracking.Finally,using the Qt development platform,the UWB mobile node positioning system is designed and developed,and the system is used to test the proposed positioning and tracking algorithm.The experimental results show that the UWB mobile node positioning system works well under the premise of meeting the design requirements.The proposed positioning and tracking algorithm both improve the original algorithm and improve the accuracy of the algorithm.