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

作     者：Guantong Guan Guohua Chen 

作者机构：College of Mechanical and Electrical EngineeringBeijing University of Chemical TechnologyBeijingPeople’s Republic of China 

出 版 物：《Journal of Control and Decision》 (控制与决策学报（英文）)

年 卷 期：2023年第10卷第4期

页      面：547-559页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

主　　题：Indoor UAV positioning UWB BP neural networks least squares estimation extended Kalmanfiltering 

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.