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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.

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