UAV/RTS system based on MMCPF theory for fast and precise determination of position and orientation

Zhang, Di; Fang, Tianye; Ai, Jiahao; Wang, Yapeng; Zhou, Lv; Guo, Jiang; Wensheng, Mei; Zhao, Yehao

doi:10.1016/j.measurement.2021.110342

Cited by 8 publications

(1 citation statement)

References 26 publications

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“…Robotic Total Stations (RTSs) have gained significant attention for their versatile applications in various fields, especially in environments where the GNSS signal is unreliable [27]. In outdoor environments, RTSs are the preferred choice to obtain high-accuracy measurements in the order of millimeters.…”

Section: Introductionmentioning

confidence: 99%

System and Method for Reducing NLOS Errors in UWB Indoor Positioning

Wang,

Zhang,

et al. 2024

Applied Sciences

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The ultra-wideband (UWB) technology has been increasingly recognized as an efficacious strategy for Indoor Positioning Systems (IPSs). However, the accuracy of the UWB system can be severely degraded by non-line-of-sight (NLOS) errors. In this study, we proposed a new method to reduce the UWB positioning error in such an indoor environment. We developed a system consisting of a Robotic Total Station (RTS), four UWB base stations, a moving target (including a prism and a UWB tag), and a PC. The observed coordinates of the moving target, captured using millimeter precision from an RTS device, served as the ground truth for calculating the positioning errors of the UWB tag. In a significant NLOS scenario, the UWB’s three-dimensional positioning error was identified to exceed the nominal value declared by the manufacturer by a factor of more than three. A detailed analysis revealed that each coordinate component’s error distribution pattern demonstrated considerable variance. To reduce the NLOS error, we designed a combined multilayer neural network that simultaneously fits errors on all three coordinate components and three separate multilayer networks, each dedicated to optimizing errors on a single coordinate component. All networks were trained and verified by benchmark errors obtained from the RTS. The results showed that neural networks outperform the traditional methods, attributed to their strong nonlinear modelling ability, thereby significantly improving the external accuracy by an average reduction in RMSE by 61% and 72%. It is evident that the proposed separate networks would be more suitable for NLOS positioning problems than a combined network.

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Section: Introductionmentioning

confidence: 99%