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基于遗传退火和聚类分析的超宽带室内定位优化算法

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

作者信息

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

机构信息

College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China.

出版信息

Front Neurorobot. 2022 Jul 26;16:715440. doi: 10.3389/fnbot.2022.715440. eCollection 2022.

DOI:10.3389/fnbot.2022.715440
PMID:35958683
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9360920/
Abstract

Indoor location information is an indispensable parameter for modern intelligent warehouse management and robot navigation. Indoor wireless positioning exhibits large errors due to factors such as indoor non-line-of-sight (NLOS) obstructions. In the present study, the error value under the time of arrival (TOA) algorithm was evaluated, and the trilateral positioning method was optimized to minimize the errors. An optimization algorithm for indoor ultra-wideband (UWB) positioning was designed, which was referred as annealing evolution and clustering fusion optimization algorithm. The algorithm exploited the good local search capability of the simulated annealing algorithm and the good global search capability of the genetic algorithm to optimize cluster analysis. The optimal result from sampled data was quickly determined to achieve effective and accurate positioning. These features reduced the non-direct aiming error in the indoor UWB environment. The final experimental results showed that the optimized algorithm significantly reduced noise interference as well as improved positioning accuracy in an NLOS indoor environment with less than 10 cm positioning error.

摘要

室内位置信息是现代智能仓库管理和机器人导航不可或缺的参数。由于室内非视距（NLOS）障碍物等因素，室内无线定位存在较大误差。在本研究中，评估了到达时间（TOA）算法下的误差值，并对三边定位方法进行了优化以最小化误差。设计了一种室内超宽带（UWB）定位优化算法，即退火进化与聚类融合优化算法。该算法利用模拟退火算法良好的局部搜索能力和遗传算法良好的全局搜索能力来优化聚类分析。快速确定采样数据的最优结果以实现有效且精确的定位。这些特性减少了室内UWB环境中的非直接瞄准误差。最终实验结果表明，优化算法在NLOS室内环境中显著降低了噪声干扰，并提高了定位精度，定位误差小于10厘米。

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