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>