UWB Positioning and Tracking Data Set

UWB Positioning Data Set UWB localization data set contains measurements from four different indoor environments. The data set contains measurements that can be used for range-based localization evaluation in different indoor environments. Measurement system The measurements were made using 9 DW1000 UWB transceivers (DWM1000 modules) connected to the networked RaspberryPi computer using in-house radio board SNPN_UWB. 8 nodes were used as localization anchor nodes with fixed locations in individual indoor environment and one node was used as a mobile localization tag. Each UWB node is designed arround the RaspberryPi computer and are wirelessly connected to the measurement controller (e.g. laptop) using Wi-Fi and MQTT communication technologies. All localization tag positions were generated beforehand to as closelly resemble the human walking path as possible. All walking path points are equally spaced to represent the equidistand samples of a walking path in a time-domain. On a Figure 2 there is an example of complete indoor localization measurement setup. Blue points represent tag positions and black crosses represent reference anchors positions. The sampled walking path (measurement TAG positions) are included in a downloadable data set file under downloads section. Folder structure is represented below this text. Folder contains four subfolders named by the indoor environments measured during the measurement campaign. Each environment folder has a anchors.csv file with anchor names and locations, subfolder floorplan with floorplan.dxf (AutoCAD format) and floorplan.png, subfolder measurements and walking_path.csv file with tag measurement positions. Measurements subfolder contains subfolders named by the tag positions form the walking_path.csv. There is exactly the same number of folders in folder measurements as is the number of measurement points in the walking_path.csv. Each measurement subfolder contains 48 .csv files named by communication channel and anchor used for those measurements. For example: ch1_A1.csv contains all measurements at selected tag location with anchor A1 on UWB channel ch1. location0 - anchors.csv - floorplan.dxf - floorplan.png - floorplan_track.jpg - walking_path.csv - measurements + 1.07_9.37_1.2 ++ ch1_A1.csv ++ ch7_A8.csv ++ ... + 1.37_9.34_1.2 ++ ... + ... location1 - ... location2 - ... location3 - ... Data format Measurements are saved in .csv files. Each file starts with a header, where first line represents the version of the file and the second line represents the data column names. The column names have a missing column name. Actual column names included in the .csv files are: TAG_ID ANCHOR_ID X_TAG Y_TAG Z_TAG X_ANCHOR Y_ANCHOR Z_ANCHOR NLOS RANGE FP_INDEX RSS RSS_FP FP_POINT1 FP_POINT2 FP_POINT3 STDEV_NOISE CIR_POWER MAX_NOISE RXPACC CHANNEL_NUMBER FRAME_LENGTH PREAMBLE_LENGTH BITRATE PRFR PREAMBLE_CODE CIR (starts with this column; all columns until the end of the line represent the channel impulse response) Citation If you are using our data set in your research, citation of the following paper would be greatly appreciated. Plain text: K. Bregar and M. Mohorčič, "Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices," in IEEE Access, vol. 6, pp. 17429-17441, 2018. doi: 10.1109/ACCESS.2018.2817800 keywords: {Computational modeling;Convolutional neural networks;Distance measurement;Estimation;Heuristic algorithms;Performance evaluation;Prediction algorithms;Channel impulse response;convolutional neural network;deep learning;indoor localization;non-line-of-sight;ranging error mitigation;ultra-wide band}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8320781&isnumber=8274985 BibTex: @ARTICLE{8320781, author={K. Bregar and M. Mohorčič}, journal={IEEE Access}, title={Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices}, year={2018}, volume={6}, number={}, pages={17429-17441}, keywords={Computational modeling;Convolutional neural networks;Distance measurement;Estimation;Heuristic algorithms;Performance evaluation;Prediction algorithms;Channel impulse response;convolutional neural network;deep learning;indoor localization;non-line-of-sight;ranging error mitigation;ultra-wide band}, doi={10.1109/ACCESS.2018.2817800}, ISSN={}, month={},} Authors and License Author of data set in this repository is Klemen Bregar, [e-mail]. Copyright (C) 2020 SensorLab, Jožef Stefan Institute, [e-mail]. Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Funding The research leading to these results has received funding from the European Horizon 2020 Programme project eWINE under grant agreement No. 688116.

UWB定位数据集
本超宽带（UWB，Ultra Wide Band）定位数据集包含来自四种不同室内环境的测量数据，可用于开展不同室内环境下基于距离的定位评估研究。

测量系统
本次测量采用9台DW1000超宽带收发器（DWM1000模块），通过自研射频板SNPN_UWB连接至联网的树莓派（RaspberryPi）计算机。其中8个节点作为固定部署于各室内环境的定位锚节点，剩余1个节点作为移动定位标签。每台UWB节点基于树莓派计算机设计，通过Wi-Fi与MQTT通信技术无线连接至测量控制器（如笔记本电脑）。所有定位标签的预设位置均尽可能模拟人类行走路径，且各行走路径点间距均等，以表征时域下行走路径的等距采样样本。图2展示了完整的室内定位测量搭建方案示例：蓝色点代表标签位置，黑色叉号代表参考锚节点位置。采样得到的行走路径（即标签测量位置）已包含于下载专区的可下载数据集文件中。

文件夹结构
数据集根目录包含4个子文件夹，分别以本次测量活动中的室内环境命名。每个环境文件夹内均包含：anchors.csv文件（存储锚节点名称与位置信息）、floorplan子文件夹（内含floorplan.dxf【AutoCAD格式】与floorplan.png平面图文件）、measurements子文件夹，以及存储标签测量位置的walking_path.csv文件。measurements子文件夹下包含多个以walking_path.csv中的标签位置命名的子文件夹，其数量与walking_path.csv中的测量点总数完全一致。每个测量子文件夹内均包含48个以通信信道与所用锚节点命名的.csv文件，例如：ch1_A1.csv包含了在指定标签位置下，使用锚节点A1在UWB信道ch1上采集的所有测量数据。文件夹层级结构示例如下：
location0
├─ anchors.csv
├─ floorplan/
│ ├─ floorplan.dxf
│ ├─ floorplan.png
│ └─ floorplan_track.jpg
├─ walking_path.csv
└─ measurements/
├─ 1.07_9.37_1.2/
│ ├─ ch1_A1.csv
│ ├─ ch7_A8.csv
│ └─ ...
├─ 1.37_9.34_1.2/
│ └─ ...
└─ ...
location1/
└─ ...
location2/
└─ ...
location3/
└─ ...

数据格式
测量数据以.csv格式存储。每个文件均以文件版本行作为首行，第二行为数据列名。部分列名存在缺失项，本数据集.csv文件实际包含的列名如下：TAG_ID、ANCHOR_ID、X_TAG、Y_TAG、Z_TAG、X_ANCHOR、Y_ANCHOR、Z_ANCHOR、NLOS（非视距，Non-Line-of-Sight）、RANGE（测距值）、FP_INDEX、RSS（接收信号强度，Received Signal Strength）、RSS_FP、FP_POINT1、FP_POINT2、FP_POINT3、STDEV_NOISE（噪声标准差）、CIR_POWER（信道冲激响应功率）、MAX_NOISE（最大噪声）、RXPACC、CHANNEL_NUMBER（信道编号）、FRAME_LENGTH（帧长度）、PREAMBLE_LENGTH（前导码长度）、BITRATE（比特率）、PRFR、PREAMBLE_CODE（前导码编码）、CIR（信道冲激响应，自本列起，后续所有列均为信道冲激响应数据）。

引用说明
若您在研究中使用本数据集，敬请引用以下论文：
纯文本格式：
K. Bregar 和 M. Mohorčič, "Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices", 发表于 IEEE Access, 第6卷, 第17429-17441页, 2018年.
DOI: 10.1109/ACCESS.2018.2817800
关键词：{Computational modeling;Convolutional neural networks;Distance measurement;Estimation;Heuristic algorithms;Performance evaluation;Prediction algorithms;Channel impulse response;convolutional neural network;deep learning;indoor localization;non-line-of-sight;ranging error mitigation;ultra-wide band}
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8320781&isnumber=8274985

BibTex格式：
@ARTICLE{8320781,
author={K. Bregar and M. Mohorčič},
journal={IEEE Access},
title={Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices},
year={2018},
volume={6},
number={},
pages={17429-17441},
keywords={Computational modeling;Convolutional neural networks;Distance measurement;Estimation;Heuristic algorithms;Performance evaluation;Prediction algorithms;Channel impulse response;convolutional neural network;deep learning;indoor localization;non-line-of-sight;ranging error mitigation;ultra-wide band},
doi={10.1109/ACCESS.2018.2817800},
ISSN={},
month={},
}

作者与许可
本仓库数据集的作者为Klemen Bregar，邮箱：[e-mail]。
版权所有 © 2020 约泽夫·斯特凡研究所传感器实验室（SensorLab, Jožef Stefan Institute），邮箱：[e-mail]。
本作品采用知识共享署名-非商业使用-相同方式共享4.0国际许可协议进行许可。

资助信息
本研究成果的研发工作获欧洲地平线2020计划项目eWINE资助，项目编号：688116。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于室内定位和跟踪的UWB（超宽带）数据集，包含来自四个不同室内环境的测量数据，适用于基于范围的定位算法评估。数据采集使用9个UWB节点，其中8个固定锚点和1个移动标签，模拟人类行走路径，并提供详细的CSV文件，涵盖距离、信道脉冲响应等多维信息。数据集结构清晰，包括锚点位置、行走路径和测量文件，支持室内定位研究，特别是非视距误差缓解和深度学习应用。