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

Abstract

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 problems such 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.

Keywords:

• Indoor UAV positioning
• UWB
• BP neural networks
• least squares estimation
• extended Kalman filtering

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Beijing University of Chemical Technology [0103/21570118000].

Notes on contributors

Guantong Guan

Guantong Guan received his B.Eng. degree in Mechanical Manufacture and Automation from Beijing University of Chemical Technology 2019. He is currently pursuing the M.Eng. degree with the major of Mechanical Engineering at Beijing University of Chemical Technology. His research interests include indoor positioning technology for UAV, autonomous obstacle avoidance for UAV.

Guohua Chen

Guohua Chen received his B.Eng. degree from Shenyang Institute of Technology in 1996, M.Eng. degree from Southwest University of Science and Technology in 2002 and D.Eng. degree from Beijing Institute of Technology in 2005. He is currently a professor in the College of Mechanical and Electrical Engineering at Beijing University of Chemical Technology, head of the Department of Robotics Engineering and director of the Intelligent Robotics and Unmanned Technology Laboratory. His research interests include intelligent inspection and machine vision, intelligent robotics, unmanned technology, and intelligent equipment and control.