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.
基金
supported by Beijing University of Chemical Technology[0103/21570118000].
