Research On Bias-suppression Of Wireless Location Algorithm For UAV

Unmanned Aerial Vehicle(UAV)is an unmanned aircraft operated by radio remote control equipment and self-supporting programmed control equipment.With the advantages of small size,low cost and convenient use,UAV has been widely used in various scenarios.As the key technology of UAV's application,the wireless location of UAV has always been the focus of many scholars.The wireless location of UAV in this paper is to locate position UAV through signal transmission between UAV and base station.In the course of positioning,the signal is disturbed by noise,leading to deviation in positioning.Therefore,how to reduce the influence of noise on UAV's wireless location and get better location performance is the main research topic in this paper.On the basis of the existing algorithms,the location algorithm of Line of Sight(LOS)communication and Non Line of Sight(NLOS)communication is studied and analyzed respectively.The main contents of this paper are as follows:(1).The research status of wireless location algorithms at home and abroad is briefly summarized.The geometric models of various wireless location methods and the basic algorithm of wireless location are studied and analyzed.(2).The Chan algorithm,Fang algorithm and Taylor series expansion method under LOS communication environment are studied and analyzed,and their calculation process in threedimensional space is deduced in detail.Through simulation,the disadvantages and advantages of the three algorithms are analyzed.In view of the shortcomings that Fang algorithm can not use redundant base stations to measure data and the advantages of low computational complexity,this paper studies an improved algorithm based on Fang: residual weighting algorithm based on Fang.The algorithm first divides all the base stations that are involved in the location,then locates the measured data of each group of base stations to get the estimated position and calculate the residual of each group by Fang algorithm.Finally,it uses the reciprocal of residuals as weights to obtain the final result by weighting the estimated positions of each group.The simulation results show that the localization performance of the algorithm is higher than that of Chan algorithm,Fang algorithm and Taylor series expansion method,and the positioning performance of the algorithm is improved by about 20% compared with the Chan algorithm.(3).The advantages and disadvantages of Kalman filtering for UAV positioning under NLOS propagation environment are studied and analyzed.Aiming at the defect that traditional Kalman filtering cannot effectively eliminate NLOS propagation error,this paper studies a uav wireless positioning algorithm based on two-step Kalman filtering.the first Kalman filtering can change the gain of Kalman to reduce the estimation result and eliminate the communication error of NLOS.The second Kalman filtering can track and locate the UAV,and provide a precise initial iteration value for the first Kalman filtering.At the same time,the second Kalman filter can reduce the network burden of the base station.The simulation shows that the algorithm can effectively eliminate the NLOS propagation error and has better tracking and positioning performance.When the noise mean is 20 m,the performance of the algorithm is improved by about 21% compared with the traditional Kalman filtering,and the percentage of performance improvement increases with the increase of noise mean.