Dataset: Ultra-Wideband Ranging Measurements Acquired With Three Different Platforms (Qorvo, TDSR, 3db Access)

  • Flueratoru Laura (University Politehnica of Bucharest) (Creator)
  • Elena-Simona Lohan (Creator)
  • Niculescu Dragoș (University Politehnica of Bucharest) (Creator)



This dataset contains distance measurements acquired with three different ultra-wideband (UWB) platforms developed by Qorvo (DW3000), TDSR (P452A), and 3db Access (3DB6380C) at the same locations. The dataset accompanies the paper: "Challenges in Platform-Independent UWB Ranging and Localization Systems" by Laura Flueratoru, Elena Simona Lohan, Dragoș Niculescu, published in the 16th ACM Workshop on Wireless Network Testbeds, Experimental evaluation and Characterization (WiNTECH) 2022. If you find this dataset useful, please consider citing our paper. The dataset ( contains the following directories: parallel_measurements -- The actual dataset, containing all the measurements acquired with the three UWB platforms at the same locations. This directory contains three subdirectories, one for each device. The structure of the subdirectory of each platform is the following: [location_name] [LOSi/NLOSi] -- where i is the index of the recording and LOS/NLOS indicates whether that recording was acquired in LOS or NLOS info.csv -- CSV file which contains information about the recording, such as: the device it was acquired with, the LOS/NLOS condition, the type of obstruction (if any), etc. unaligned_processed_data.csv -- CSV file which contains the data. Each row has the following fields: timestamp, true distance, measured distance, time of arrival index, channel impulse response (stored as a list), and the LOS/NLOS label. split_train_test_val -- Datasets that were used to train and test the models from Section 4 from the paper. The datasets contain the same information as the directory parallel_measurements, only aligned to the TOA and randomized according to the procedure described in the paper. We include the generated sets to ensure the repeatability of our results. trained_models_error_prediction -- Models trained for error prediction that were used to obtain the results from Section 4 from the paper. We also provide code examples for reading the data, training and testing the models, and analyzing the data at the following repository: The accompanying code is subject to change in the case of bugs/errors. For more information about how the measurements were acquired, please refer to the file documentation_dataset.pdf, which includes detailed information about each of the rooms, the device setup, the structure of the directories, etc. For any questions, do not hesitate to contact the authors of the paper. Note: The dataset (in the parallel_measurements directory) contains measurements acquired with the devices at fixed locations and also "free movement" measurements, during which one of the devices was moved freely around a certain area. Therefore, free-movement recordings with the same name but from different devices were not acquired at exactly the same locations, only in the same rooms. The free-movement recordings were not used in the paper (because they do not contain ground truth distances), but we nevertheless include them in this dataset, as they can be useful to test future algorithms.
Koska saatavilla12 elok. 2022

Field of science, Statistics Finland

  • 213 Sähkö-, automaatio- ja tietoliikennetekniikka, elektroniikka
  • Challenges in platform-independent UWB ranging and localization systems

    Flueratoru, L., Lohan, E. S. & Niculescu, D., 26 lokak. 2022, WiNTECH 2022 - Proceedings of the 2022 16th ACM Workshop on Wireless Network Testbeds, Experimental evaluation and CHaracterization, Part of MobiCom 2022. ACM, s. 9-15 7 Sivumäärä

    Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

    Open access
    2 Lataukset (Pure)

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