Dataset of continuous human activities performed in arbitrary directions collected with a distributed radar network of five nodes

Please review the document README_v3.pdfThe data can be extracted with the MATLAB Live Script dataread.mlx, or in Python with the file dataread_numpy.py. <br><strong>Update 2025-09-16 (by Simin Zhu):</strong>The code and processed radar data used in publication [4] have been added here as supplementary material (Zip file named as the publication). Before using them, please read the provided README file. The supplement contains a separate README, and you will need to unpack the compressed folder to access the contents.<br>Referencing the datasetPlease click the orange "Citation" button above.Paper references are: [1] Guendel, R.G., Fioranelli, F.,Yarovoy, A.: <em>Distributed radar fusion and recurrent networks for classification of continuous human activities</em>. IET Radar Sonar Navig. 1–18 (2022). https://doi.org/10.1049/rsn2.12249 <br>[2] R. G. Guendel, F. Fioranelli and A. Yarovoy, "<em>Evaluation Metrics for Continuous Human Activity Classification Using Distributed Radar Networks</em>," 2022 IEEE Radar Conference (RadarConf22), 2022, pp. 1-6, doi: 10.1109/RadarConf2248738.2022.9764181.<br>[3] R. G. Guendel, M. Unterhorst, E. Gambi, F. Fioranelli and A. Yarovoy, "<em>Continuous human activity recognition for arbitrary directions with distributed radars</em>," 2021 IEEE Radar Conference (RadarConf21), 2021, pp. 1-6, doi: 10.1109/RadarConf2147009.2021.9454972.<br>[4] S. Zhu, R. G. Guendel, A. Yarovoy and F. Fioranelli, "Continuous Human Activity Recognition With Distributed Radar Sensor Networks and CNN–RNN Architectures," in <em>IEEE Transactions on Geoscience and Remote Sensing</em>, vol. 60, pp. 1-15, 2022, Art no. 5115215, doi: 10.1109/TGRS.2022.3189746.<br>请查阅文档README_v3.pdf。该数据集可通过MATLAB 实时脚本（MATLAB Live Script）dataread.mlx提取，亦可借助Python脚本dataread_numpy.py进行读取。<br>**2025年9月16日更新（Simin Zhu 提交）：**本页面现已补充上传论文[4]中使用的代码与已处理雷达数据，压缩包命名与对应论文一致。使用前请务必阅读附带的README文件，该补充材料包含独立的README文件，需解压压缩包后方可访问其中内容。<br>数据集引用说明：请点击页面上方橙色的"引用（Citation）"按钮获取标准引用格式。论文参考文献如下：<br>[1] Guendel, R.G., Fioranelli, F., Yarovoy, A.: *分布式雷达融合与循环网络用于连续人类活动分类*，《IET雷达、声纳与导航》，1–18 (2022). https://doi.org/10.1049/rsn2.12249<br>[2] R. G. Guendel, F. Fioranelli 与 A. Yarovoy，*面向分布式雷达网络的连续人类活动分类评估指标*，2022 IEEE雷达会议（RadarConf22），2022，pp.1-6，doi: 10.1109/RadarConf2248738.2022.9764181.<br>[3] R. G. Guendel, M. Unterhorst, E. Gambi, F. Fioranelli 与 A. Yarovoy，*面向任意运动方向的分布式雷达连续人类活动识别*，2021 IEEE雷达会议（RadarConf21），2021，pp.1-6，doi: 10.1109/RadarConf2147009.2021.9454972.<br>[4] S. Zhu, R. G. Guendel, A. Yarovoy 与 F. Fioranelli，*基于分布式雷达传感器网络与卷积循环神经网络（CNN-RNN）架构的连续人类活动识别*，发表于《IEEE地球科学与遥感汇刊》，第60卷，pp.1-15，2022，文章编号5115215，doi: 10.1109/TGRS.2022.3189746.

搜集汇总

背景与挑战

背景概述

该数据集通过一个包含五个节点的分布式雷达网络收集，专注于记录人类在任意方向上进行的连续活动，用于人类活动识别（HAR）研究。数据集包含原始雷达数据和相关处理代码（Matlab和Python格式），适用于机器学习分类任务，并已用于多篇学术论文中，支持辅助生活和工程应用。