An improved LSE-EKF optimisation algorithm for UAV UWB positioning in complex indoor environments  

作　　者：Guantong Guan Guohua Chen 

机构地区：[1]College of Mechanical and Electrical Engineering,Beijing University of Chemical Technology,Beijing,People’s Republic of China

出　　处：《Journal of Control and Decision》2023年第4期547-559,共13页控制与决策学报（英文）

基　　金：supported by Beijing University of Chemical Technology[0103/21570118000].

摘　　要：With the increasing application of UAVs,UAV positioning technology for indoor complex environment has become a hot research issue in the industry.The traditional UWB positioning technology is affected by problems such as multipath effect and non-line-of-sight propagation,and its application in complex indoor environments has problemssuch as poor positioning accuracy and strong noise interference.We propose an improved LSE-EKF optimisation algorithm for UWB positioning in indoor complex environments,which optimises the initial measurement data through a BP neural network correction model,then optimises the coordinate error using least squares estimation to find the best pre-located coordinates,finally eliminates the interference noise in the pre-located coordinate signal through an EKF algorithm.It has been verified by experiments that the evaluation index can be improved by more than 9%compared with EKF algorithm data,especially under non-line-of-sight(NLOS)conditions,which enhances the possibility of industrial application of indoor UAV.

关 键 词：Indoor UAV positioning UWB BP neural networks least squares estimation extended Kalmanfiltering 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]