Supplementary Research Data for the Paper entitled Identification of NLOS and Multi-path Conditions in UWB Localization using Machine Learning Methods

This experimental research data-set was used to present the multi-label classification results of UWB ranging system in our journal article entitled “Identification of NLOS and Multi-path Conditions in UWB Localization using Machine Learning Methods”. The research data includes the extracted features of UWB experimental data including their respective labels and the corresponding source code for the python machine learning library scikit-learn. The article was published in the special issue entitled “Recent Advances in Indoor Localization Systems and Technologies” at computing and artificial intelligence section, applied sciences journal, MDPI.

本实验研究数据集用于展示我们发表于期刊论文《基于机器学习方法的UWB定位中非视距（Non-Line-of-Sight, NLOS）与多路径条件识别》中的超宽带（Ultra Wide Band, UWB）测距系统多标签分类结果。本研究数据集包含UWB实验数据的提取特征及其对应标签，以及适配Python机器学习库scikit-learn的对应源代码。该论文发表于MDPI旗下《应用科学》（Applied Sciences）期刊计算与人工智能专题版块的特刊《室内定位系统与技术最新进展》。