AUTHOR=Guo Hua , Li Mengqi , Zhang Xuejing , Gao Xiaotian , Liu Qian 

TITLE=UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis

JOURNAL=Frontiers in Neurorobotics

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2022.715440

DOI=10.3389/fnbot.2022.715440

ISSN=1662-5218

ABSTRACT=Indoor location information is an indispensable important parameter for modern intelligent warehouse management and robot navigation. Due to factors like indoor non-line-of-sight obstructions, indoor wireless positioning often has big errors. To eliminate the influence of such errors, this paper analyzes the error form under the TOA algorithm, and conducts preliminary algorithm optimization of the trilateral positioning method. On this basis, the paper also designs an optimization algorithm for indoor ultra-wideband (UWB) system positioning -annealing evolution and clustering algorithm fusion optimization algorithm. By giving full play to the strong local search capability of simulated annealing algorithm and good global search capability of genetic algorithm to optimize cluster analysis, the optimal value among massive sampled data is quickly found to achieve effective and accurate positioning, thereby reducing the non-direct aiming error in the indoor ultra-wideband environment. The final experimental results show that this scheme can significantly reduce noise interference, improve positioning accuracy in an ultra-wideband environment, with positioning error is less than 10 cm.