一种复杂环境下UWB测距误差预测方法
A UWB ranging error prediction method in complex environments
李圣令 1刘克中 2陈聪 3王一飞 3王国宇 1陈默子 4郑凯1
作者信息
- 1. 武汉理工大学 航运学院,武汉 430063
- 2. 武汉理工大学 航运学院,武汉 430063;内河航运技术湖北省重点实验室,武汉 430063;国家水运安全工程技术研究中心,武汉 430063
- 3. 中船邮轮科技发展有限公司,上海 200137
- 4. 武汉理工大学 航运学院,武汉 430063;内河航运技术湖北省重点实验室,武汉 430063
- 折叠
摘要
针对复杂室内环境中,多径干扰和视距遮挡影响室内定位精度的问题,提出一种复杂环境下 UWB测距误差的预测方法:根据超宽带(UWB)室内定位方法抗干扰能力强、定位精度高的特点,从理论上分析影响超宽带信号测距精度的因素;然后通过模型量化分析每种影响因素对测距误差的影响程度,改进以往通过信道脉冲响应特征分析单一的视距遮挡参数来提高定位精度的补偿算法;最后从数据挖掘和机器学习的角度出发,将多径效应、视距遮挡、标签与基站的距离、标签移动速度、标签与基站天线俯仰角以及气象因素(温度、湿度、压强)等对测距精度造成影响的重要因素作为特征进行分类和归类,并使用梯度提升决策树模型对数据集进行训练和预测.实验结果表明,该模型可以根据当前状态下的组合特征值估计出测距误差值,将当前预测的误差值补偿到测量值上,可以有效提高超宽带室内定位的精度.
Abstract
Aiming at the problem that multipath interference and line of sight occlusion affect indoor positioning accuracy in complex indoor environments,the paper proposed an ultra-wide band(UWB)ranging error prediction method in complex environments:based on the advantages of strong anti-interference ability and high positioning accuracy of UWB indoor positioning,the factors that affect the ranging accuracy of UWB signals were theoretically analyzed;then the impact of each influencing factor on the ranging error was quantitatively analyzed through a model in order to improve the compensation algorithm for upgrading the positioning accuracy by analyzing a single line of sight occlusion parameter through channel pulse response characteristics in the past;finally,from the perspective of data mining and machine learning,the important factors including multipath effects,line of sight occlusion,distance between tags and base stations,tag movement speed,tag and base station antenna pitch angle,and meteorological factors(temperature,humidity,pressure)that affect ranging accuracy were classified and categorized as features,and a gradient boosting decision tree model was used to train and predict the dataset.Experimental result showed that the proposed model would estimate the ranging error value based on the combined feature values in the current state,and compensate the current predicted error value to the measured value,which could effectively improve the accuracy of UWB indoor positioning.
关键词
室内定位/超宽带(UWB)定位/测距误差/梯度提升决策树/机器学习Key words
indoor positioning/ultra-wide band(UWB)localization/ranging error/gradient boosting decision tree/machine learning引用本文复制引用
基金项目
国家自然科学基金面上项目(51979216)
湖北省自然科学基金创新群体项目(2021CFA001)
湖北省自然科学基金青年项目(20221J0059)
出版年
2024
NETL
NSTL
万方数据