Research On M-AOA/TDOA Wireless Location Algorithm Based On Information Fusion

With the continuous development of wireless technology,wireless positioning technology has gradually entered people’s field of vision.There are more and more application scenarios based on wireless positioning technology,but in the channel transmission environment,due to the unfavorable factors that have a great impact on wireless positioning such as noise,multipath effect,non-line-of-sight transmission,and multi-access interference in the complex environment,the performance of the positioning system is affected to a certain extent.In view of the existing unfavorable factors,this thesis studies the high-precision positioning algorithm on the basis of analyzing its principle and influence.This thesis mainly studies the wireless positioning algorithm based on information fusion based on the positioning algorithm based on arrival time difference and the positioning algorithm of arrival angle,and the main research content is as follows:1.Facing the direct wave channel transmission environment,in order to improve the positioning accuracy of the wireless positioning algorithm in the direct wave channel transmission environment,this thesis uses the Levenberg-Marquardt(LM)to solve the Angle of Arrival(AOA)positioning equation and the multi-angle of arrival/Time Difference of Arrival(M-AOA/TDOA)positioning equation.Among them,the AOA positioning algorithm based on Leeuwenberg-Marquardt uses the LM algorithm to solve the AOA positioning equation system compared with the traditional maximum likelihood estimation algorithm,and when the method solves the positioning equation system,the introduction of the trust region can solve the problem of easy divergence caused by poor Geometric Dilution Precision(GDOP)to a certain extent,and improve the positioning accuracy to a certain extent.In addition,the M-AOA/TDOA positioning algorithm based on information fusion is a fusion positioning algorithm that integrates AOA,TDOA,Received Signal Strength Indicator(RSSI)and other information,the algorithm screens the number of positioning base stations through RSSI information,constructs a positioning equation system through AOA and TDOA information,and uses LM algorithm to solve the positioning equation system.The accuracy of the positioning system is improved by means of information fusion.Through simulation results,it is found that the positioning accuracy of the algorithm can be close to the Cramer-Rao Lower Bound(CRLB).2.Focusing on the non-line-of-sight channel transmission environment,this thesis proposes an M-AOA/TDOA positioning algorithm based on Unscented Kalman Filter(UKF)and an M-AOA/TDOA positioning algorithm based on Cubature Kalman Filter(CKF)to solve the problem of low accuracy of wireless positioning algorithm in non-line-of-sight environment.Aiming at the NLOS(Non-Line of Sight,NLOS)and LOS(Line of Sight,LOS)mixed propagation positioning environment,the M-AOA/TDOA positioning algorithm introduces speed information and motion state information,and respectively uses UKF and CKF two nonlinear Kalman filter algorithms to predict,which effectively improves the positioning performance and positioning accuracy of the algorithm in the non-line-of-sight environment.Among the two algorithms,the M-AOA/TDOA positioning algorithm based on UKF has better real-time performance due to the fast prediction convergence speed of UKF,while the CKF algorithm adopts the principle of spherical radial volume to solve,so the M-AOA/TDOA positioning algorithm based on CKF has higher positioning accuracy.In addition,the data acquisition module built by DW1000 chip is also used to measure the AOA and TDOA information of the positioning target in the actual environment,and the positioning performance of the two algorithms is verified through experiments,compared with the traditional AOA or TDOA positioning algorithm,the positioning performance and positioning accuracy of the two positioning algorithms proposed in this thesis have been improved.