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24 results for “UWB”
UWB-IODA project, Work package 1: IR-UWB optimized pulses
<p>The data files contain optimized UWB waveforms using B-spline functions. The spectral efficiency of each waveform is maximized under the constraint of the spectral mask defined by the FCC/ECC regulation authorities.</p>
UWB-IODA project: Datasets associated to WP2
<p>The signal acquisition is carried out using an IR-UWB sensor module Xethru X4, from Novelda, Norway, having the following parameters:<br> Output power −12.6 dBm<br> Center frequency 8.748 GHz<br> Pulse repetition frequency 400 MHz<br> Bandwidth (–10 dB) 2.95 GHz<br> Range resolution 6.4 mm<br> Beamwidth 65°<br> Staggered PRF sequence length 220 cycles<br> No. of antenna arrays per radar chip 1 Tx & 1 Rx</p> <p>The transmitted signals reflected by the human back contain the information related to the motion caused by the human heart beats. The backscattered signals are then stored in the PC through a micro-USB cable. The distance of the human to the radar sensor is kept between 0.5 and 1m due to the short distance between the driver and the seat. During the experiments, the subjects were resting for most of the time and also talking and moving slightly their bodies for certain time intervals.</p> <p>The five data files corresponds to the five subjects involved in the experiments, having the following characteristics:</p> <p>Gender Age (years) Height (cm) Weight (kg) <br> M 33 182 72<br> M 21 173 73<br> M 28 174 77<br> F 26 170 60<br> M 24 165 62</p> <p>All of these individuals were healthy and without any special health conditions or disabilities.</p>
Resolution Enhancement of UWB Time-Reversal Microwave Imaging in Dispersive Environments (dataset)
<p>These files are the simulation data used to create the figures illustrated in the journal paper with the same title which has been accepted for publication as a regular paper in IEEE Transactions on Computational Imaging. Each filename indicates the figure number associated with the file. These files are text files. The column structure of each file is described in the README file.</p>
UWB Positioning and Tracking Data Set
<p><strong># UWB Positioning and Tracking Data Set</strong></p> <p>UWB positioning data set contains measurements from four different indoor environments. The data set contains measurements that can be used for range-based positioning evaluation in different indoor environments.</p> <p> </p> <p><strong># Measurement system</strong></p> <p>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 positioning anchor nodes with fixed locations in individual indoor environment and one node was used as a mobile positioning tag.</p> <p>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.</p> <p>All 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. The sampled walking path (measurement TAG positions) are included in a downloadable data set file under downloads section.</p> <p> </p> <p><strong># Folder structure</strong></p> <p>Folder structure is represented below this text. Folder contains four subfolders named by the indoor environments measured during the measurement campaign and a folder raw_data where raw measurement data is saved. Each environment folder has a anchors.csv file with anchor names and locations, .json file data.json with measurements, file walking_path.csv file with tag positions and subfolder floorplan with floorplan.dxf (AutoCAD format), floorplan.png and floorplan_track.jpg.</p> <p>Subfolder raw_data contains raw data in subfolders named by the four indor environments where the measurements were taken. Each location subfolder contains a subfolder data where data from each tag position from the walking_path.csv is collected in a separate folder. There is exactly the same number of folders in data folder 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. The location folder contains also anchors.csv and walking_path.csv files which are identical to the files mentioned previously.</p> <p>The last folder in the data set is the technical_validation folder, where results of technical validation of the data set are collected. They are separated into 8 subfolders:</p> <p>- cir_min_max_mean</p> <p>- los_nlos</p> <p>- positioning_wls</p> <p>- range</p> <p>- range_error</p> <p>- range_error_A6</p> <p>- range_error_histograms</p> <p>- rss</p> <p> </p> <p>The organization of the data set is the following:</p> <p>data_set</p> <p>+ location0</p> <p>- anchors.csv</p> <p>- data.json</p> <p>- walking_path.csv</p> <p>+ floorplan</p> <p>- floorplan.dxf</p> <p>- floorplan.png</p> <p>- floorplan_track.jpg</p> <p>- walking_path.csv</p> <p>+ location1</p> <p>- ...</p> <p>+ location2</p> <p>- ...</p> <p>+ location3</p> <p>- ...</p> <p>+ raw_data</p> <p>+ location0</p> <p>+ data</p> <p>+ 1.07_9.37_1.2</p> <p>- ch1_A1.csv</p> <p>- ch7_A8.csv</p> <p>- ...</p> <p>+ 1.37_9.34_1.2</p> <p>- ...</p> <p>+ ...</p> <p>+ location1</p> <p>+ ...</p> <p>+ location2</p> <p>+ ...</p> <p>+ location3</p> <p>+ ...</p> <p>+ technical validation</p> <p>+ cir_min_max_mean</p> <p>+ positioning_wls</p> <p>+ range</p> <p>+ range_error</p> <p>+ range_error_histograms</p> <p>+ rss</p> <p>- LICENSE</p> <p>- README</p> <p> </p> <p><strong># Data format</strong></p> <p>Raw 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:</p> <p> </p> <p>TAG_ID</p> <p>ANCHOR_ID</p> <p>X_TAG</p> <p>Y_TAG</p> <p>Z_TAG</p> <p>X_ANCHOR</p> <p>Y_ANCHOR</p> <p>Z_ANCHOR</p> <p>NLOS</p> <p>RANGE</p> <p>FP_INDEX</p> <p>RSS</p> <p>RSS_FP</p> <p>FP_POINT1</p> <p>FP_POINT2</p> <p>FP_POINT3</p> <p>STDEV_NOISE</p> <p>CIR_POWER</p> <p>MAX_NOISE</p> <p>RXPACC</p> <p>CHANNEL_NUMBER</p> <p>FRAME_LENGTH</p> <p>PREAMBLE_LENGTH</p> <p>BITRATE</p> <p>PRFR</p> <p>PREAMBLE_CODE</p> <p>CIR (starts with this column; all columns until the end of the line represent the channel impulse response)</p> <p> </p> <p><strong># Availability of CODE</strong></p> <p>Code for data analysis and preprocessing of all data available in this data set is published on GitHub:</p> <p>https://github.com/KlemenBr/uwb_positioning.git</p> <p>The code is licensed under the Apache License 2.0.</p> <p> </p> <p><strong># Authors and License</strong></p> <p>Author of data set in this repository is Klemen Bregar, klemen.bregar@ijs.si.</p> <p>This work is licensed under a Creative Commons Attribution 4.0 International License.</p> <p> </p> <p><strong># Funding</strong></p> <p>The research leading to the data collection has been partially funded from the European Horizon 2020 Programme project eWINE under grant agreement No. 688116, the Slovenian Research Agency under Grant numbers P2-0016, J2-2507 and bilateral project with Grant number BI-ME/21-22-007.</p> <p> </p>
dairy_spatial_uwb_lying_behaviour
<p>Data that belongs to the manuscript "Detecting dairy cows' lying behavior using noisy 3D ultra-wide band positioning data" by Adriaens, Ouweltjes, Pastell, Ellen, Kamphuis.</p>
UWB Trustworthiness (Jamming and Position Dilution of Precision)
<div> <div> <div> <div>Two datasets are published. The first experiment shows a localization scenario subject to a synchronization header attack ("Jammer"). The second scenario demonstrates the effect of dilution of precision in ultra-wideband localization ("Position Dilution of Precision").</div> </div> </div> </div>
Indoor UWB CIR Data Set for Material Prediction
<p><strong>ABOUT</strong></p> <p>This data set contains spatially distributed CIR of multipath components in indoor environment acquired with ultra wideband (UWB) radio technology in microwave frequency range.<br> The data is labeled with the materials of the surfaces bounding the space (floor, ceiling, walls).<br> The data was collected for training and evaluating machine learning models for CIR-based indoor material prediction, but it may be also used for other studies based on indoor radio propagation data. </p> <p> </p> <p><strong>AUTHORS</strong></p> <p>Teodora Kocevska, Andrej Hrovat, Tomaž Javornik</p> <p>Department of Communication Systems</p> <p>Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia</p> <p> </p> <p><strong>DATA COLLECTION</strong></p> <p>The synthetic data is obtained using <a href="https://www.remcom.com/wireless-insite-em-propagation-software">Remcom Wireless InSite</a> v.3.3.3.<br> The CIR is estimated in 16,875 rooms in total. <br> These rooms belong to 5,625 distinct room types and each room type is considered in three sizes. <br> The number of distinct room types comes from the materials used for the floor, ceiling, and walls, having nine floor-ceiling material combinations and 625 wall-material combinations.</p> <p> </p> <table align="center"> <caption>Room Sizes</caption> <thead> <tr> <th scope="col"> ROOM SIZE</th> <th scope="col">FLOOR/CEILING DIMENSIONS </th> <th scope="col">WALL DIMENSIONS</th> </tr> </thead> <tbody> <tr> <td>S</td> <td>3 m x 3 m</td> <td>3 m x 3 m </td> </tr> <tr> <td>M</td> <td>5 m x 5 m</td> <td>5 m x 3 m </td> </tr> <tr> <td>L</td> <td>7 m x 7 m </td> <td>7 m x 3 m </td> </tr> </tbody> </table> <p> </p> <p><strong>MATERIALS</strong></p> <ul> <li> floor: concrete, wood, floorboard</li> <li> ceiling: concrete, plaster, wood</li> <li> walls: brick, concrete, glass, plaster, wood</li> </ul> <p> </p> <table align="center"> <caption>Electrical properties of materials*</caption> <thead> <tr> <th scope="col">MATERIAL</th> <th scope="col">RELATIVE PERMITTIVITY</th> <th scope="col">CONDUCTIVITY</th> </tr> </thead> <tbody> <tr> <td>brick</td> <td>3.75</td> <td>0.038</td> </tr> <tr> <td>concrete</td> <td>5.31</td> <td>0.120</td> </tr> <tr> <td>glass</td> <td>6.27</td> <td>0.029</td> </tr> <tr> <td>plaster</td> <td>2.94</td> <td>0.036</td> </tr> <tr> <td>wood</td> <td>1.99</td> <td>0.026</td> </tr> <tr> <td>floorboard</td> <td>3.66</td> <td>0.039</td> </tr> </tbody> </table> <p> </p> <p><strong>COMMUNICATION SYSTEM CONFIGURATION</strong></p> <p>Ultra wideband (UWB) radio technology is considered. The parameters of the communication system are set according to 802.15.4-2011** standard.<br> The configuration of system parameters is summarized as follows:</p> <table align="center"> <caption>System configuration</caption> <thead> <tr> <th scope="col"> PARAMETER</th> <th scope="col">CONFIGURATION</th> </tr> </thead> <tbody> <tr> <td>frequency</td> <td>3494.4 MHz</td> </tr> <tr> <td>bandwidth</td> <td>466.2 MHz</td> </tr> <tr> <td>Tx/Rx height</td> <td>1.5 m</td> </tr> <tr> <td>antenna type</td> <td>omni</td> </tr> <tr> <td>polarization</td> <td>vertical</td> </tr> </tbody> </table> <p> </p> <p><strong>RADIO NODE POSITIONS</strong></p> <p>The data is collected using three acquisition layouts as follows:</p> <p>1. Layout 1 </p> <p> Tx in the center of the room and Rx moved over uniform grid covering the room.</p> <p>2. Layout 2</p> <p>Tx in eight positions following circular pattern around the center of the room and Rx moved over uniform grid covering the room. </p> <p>The distance from the center of the room to the circumference of the circle is 0.5 m, and the spacing between the radio nodes is pi/4 rad.</p> <p>3. Layout 3</p> <p>Tx in four positions near the corners of the room (0.375 m from the walls) and Rx moved over uniform grid covering the room.</p> <p>The corners of the grid are 0.25 m apart from the walls and the distance between the nodes is also 0.25 m.</p> <p>Since the grid size is defined relatively to the room size, the total number of grid node positions is different in rooms with different sizes.</p> <p>The total number of grid node positions is 121, 361, and 729 in S, M, and L rooms, respectively.</p> <p> </p> <p><strong>DATA ORGANIZATION</strong></p> <p>Data is saved in .csv files. Each file starts with a header line specifying the column names. <br> The column names included in the .csv files are:</p> <p>- Column 0: layout {center, circle, corners}<br> - Column 1: tx_point_id {1} for Layout 1, {1-8} for Layout 2, and {1-4} for Layout 3<br> - Column 2: rx_point_id {1-121} in S-rooms, {1-361} in M-rooms, and {1-729} in L-rooms<br> - Column 3: 1_phase_deg NUMERIC<br> - Column 4: 1_toa_ns NUMERIC<br> - Column 5: 1_power_dbm NUMERIC<br> - Column 6: 1_power_nw NUMERIC<br> - Column 7: 2_phase_deg NUMERIC<br> - Column 8: 2_toa_ns NUMERIC<br> - Column 9: 2_power_dbm NUMERIC<br> - Column 10: 2_power_nw NUMERIC<br> - Column 11: 3_phase_deg NUMERIC<br> - Column 12: 3_toa_ns NUMERIC<br> - Column 13: 3_power_dbm NUMERIC<br> - Column 14: 3_power_nw NUMERIC<br> - Column 15: 4_phase_deg NUMERIC<br> - Column 16: 4_toa_ns NUMERIC<br> - Column 17: 4_power_dbm NUMERIC<br> - Column 18: 4_power_nw NUMERIC<br> - Column 19: 5_phase_deg NUMERIC<br> - Column 20: 5_toa_ns NUMERIC<br> - Column 21: 5_power_dbm NUMERIC<br> - Column 22: 5_power_nw NUMERIC<br> - Column 23: 6_phase_deg NUMERIC<br> - Column 24: 6_toa_ns NUMERIC<br> - Column 25: 6_power_dbm NUMERIC<br> - Column 26: 6_power_nw NUMERIC<br> - Column 27: 7_phase_deg NUMERIC<br> - Column 28: 7_toa_ns NUMERIC<br> - Column 29: 7_power_dbm NUMERIC<br> - Column 30: 7_power_nw NUMERIC<br> - Column 31: 8_phase_deg NUMERIC<br> - Column 32: 8_toa_ns NUMERIC<br> - Column 33: 8_power_dbm NUMERIC<br> - Column 34: 8_power_nw NUMERIC<br> - Column 35: 9_phase_deg NUMERIC<br> - Column 36: 9_toa_ns NUMERIC<br> - Column 37: 9_power_dbm NUMERIC<br> - Column 38: 9_power_nw NUMERIC<br> - Column 39: 10_phase_deg NUMERIC<br> - Column 40: 10_toa_ns NUMERIC<br> - Column 41: 10_power_dbm NUMERIC<br> - Column 42: 10_power_nw NUMERIC<br> - Column 43: 11_phase_deg NUMERIC<br> - Column 44: 11_toa_ns NUMERIC<br> - Column 45: 11_power_dbm NUMERIC<br> - Column 46: 11_power_nw NUMERIC<br> - Column 47: 12_phase_deg NUMERIC<br> - Column 48: 12_toa_ns NUMERIC<br> - Column 49: 12_power_dbm NUMERIC<br> - Column 50: 12_power_nw NUMERIC<br> - Column 51: 13_phase_deg NUMERIC<br> - Column 52: 13_toa_ns NUMERIC<br> - Column 53: 13_power_dbm NUMERIC<br> - Column 54: 13_power_nw NUMERIC<br> - Column 55: 14_phase_deg NUMERIC<br> - Column 56: 14_toa_ns NUMERIC<br> - Column 57: 14_power_dbm NUMERIC<br> - Column 58: 14_power_nw NUMERIC<br> - Column 59: 15_phase_deg NUMERIC<br> - Column 60: 15_toa_ns NUMERIC<br> - Column 61: 15_power_dbm NUMERIC<br> - Column 62: 15_power_nw NUMERIC<br> - Column 63: room_size_surf_m2 {9} for S-rooms, {25} for M-rooms, and {49} for L-rooms<br> - Column 64: room_size_name {S} for S-rooms, {M} for M-rooms, and {L} for L-rooms<br> - Column 65: room_shape {square}<br> - Column 66: floor_mat {concrete, wood, floorboard}<br> - Column 67: ceiling_mat {concrete, plaster, wood}<br> - Column 68: wall1_mat {brick, concrete, glass, plaster, wood}<br> - Column 69: wall2_mat {brick, concrete, glass, plaster, wood}<br> - Column 70: wall3_mat {brick, concrete, glass, plaster, wood}<br> - Column 71: wall4_mat {brick, concrete, glass, plaster, wood}</p> <p>Each row corresponds to separate radio link defined with the Tx and Rx nodes. <br> It includes information about <br> (i) the CIR-acquisition layout (position of the Txs and Rxs), <br> (ii) the Tx and Rx, <br> (iii) the CIR of 15 strongest multipath components, <br> (iv) the room geometry, and <br> (v) the materials of the surfaces bounding the space. </p> <p>- Column 0 specifies the layout. <br> The following maping is used: <br> Layout 1 -> center, <br> Layout 2 -> circle, and <br> Layout 3 -> corners.<br> - Column 1 specifies the Tx identifier.<br> - Column 2 specifies the Rx identifier.<br> - Columns 3-62 are the input attributes. <br> The input attributest represent the phase (in deg), ToA (in ns), received power (in dBm), and received power (in nW) for 15 strongest multipath components. <br> The column naming is X_Y_Z, where X is the multipath component identifier (1 to 15), Y is the propagation characteristic (phase, toa, or power), and Z is the unit (deg, ns, dbm, or nw).<br> - Column 63 specifies the surface of the room in square meters.<br> - Column 64 specifies the room size category (S, M, or L).<br> - Column 65 specifies the room-base shape.<br> - Columns 66-71 are the target attributes specifying the material of the floor, ceiling, wall 1, wall 2, wall 3, and wall 4, respectively. </p> <p> </p> <p><strong>FOLDER STRUCTURE</strong></p> <p>The folder <em>indoor_CIR_data</em> contains:<br> - one subfolder named <em>CIR_data</em><br> It contains three .csv files with CIR data named by the size of the rooms where the data is acquired.<br> For naming the .csv files the following mapping is considered:<br> - Small.csv -> S-rooms<br> - Medium.csv -> M-rooms<br> - Large.csv -> L-rooms<br> - one subfolder named <em>CIR_acquisition_details</em><br> It contains .png file with schematic representation of the radio node positions considered for obtaining the data.<br> - README.txt file</p> <p>The folder structure is:<br> - CIR_data<br> - Small.csv<br> - Medium.csv<br> - Large.csv<br> - CIR_acquisition_details<br> - radio_node_positions.png<br> - README.txt</p> <p> </p> <p><strong>REFERENCES</strong></p> <p>* R. sector of International Telecommunication Union (ITU-R), “Effects of building materials and structures on radio wave propagation above about 100 MHz,” International Telecommunication Union, ITU-R Recommendation P.2040-2, 2021.</p> <p>** IEEE, “Standard for local and metropolitan area networks–Part 15.4: Low-rate wireless personal area networks (LR-WPANs),” IEEE, Standard IEEE 802.15.4-2011, 2011.</p>
Datasets of Indoor UWB Measurements for Ranging and Positioning in Good and Challenging Scenarios
<p>This is a dataset of ranging and positioning measurements collected from an UWB development board. The Real Time Location System based on UWB is set up in a laboratory. Data were captured in the static laboratory environment with different conditions that affects to the positioning performance. In the lab, scenarios with different propagation conditions between the nodes and different geometries were set up. We consider good, challenging, and intermediate scenarios with: Line of Sight (LOS) and Non-LOS propagation conditions as well as easy and challenging geometries. These datasets may be used, for example, for investing and validating ranging and positioning algorithms in different scenarios. A detailed description is provided in the file README.pdf</p>
Data: UTrack3D: 3D Tracking Using Ultra-wideband (UWB) Radios
<p>"# UTrack3D"</p> <p><strong>Environments</strong>: Python3.7 & Matlab2021</p> <p><strong>System</strong>: Windows 11</p> <div> <h2>Install prerequisites</h2> <a href="https://github.com/yifeng361/UTrack3D#install-prerequisites"></a></div> <ul> <li> <p>Python: We test our code using Python3.7. Advanced Python versions work as well. <a href="https://www.python.org/downloads/" rel="nofollow">https://www.python.org/downloads/</a></p> </li> <li> <p>Matlab: We test our code using Matlab2021. Advanced Matlab versions work as well. The matlab is only used for performance evaluation in this code. In case you prefer not installing Matlab, several pre-generated examples are provided.</p> </li> <li> <p>Python libraries: <code>pip install -r requirements.txt</code></p> </li> </ul> <div> <h2>Running</h2> <a href="https://github.com/yifeng361/UTrack3D#running"></a></div> <ul> <li>Run script <code>run_offline_analysis.py</code> for tracking.</li> </ul> <p><code>python run_offline_track.py</code></p> <p>This reads pre-stored CIR data (./raw_data) and generates a file <code>tracking_results.mat</code> in ./output which stores the estimated trajectory and ground-truth trajectory. We provide three examples (test1, test2, test3). One can modify the following line to test a specific example.</p> <p><code>file_dir = "./raw_data/test1/"</code></p> <ul> <li>Run script <code>./matlab_analysis_scripts/evaluate_accuracy_ae.m</code> to compute error and perform visualization. This script takes <code>tracking_results.mat</code> as inputs and generates CDF error plot and trajectory visualization in the current folder.</li> </ul> <p>The CDF error plot and trajectories of three examples have already been pre-generated and put in <code>./matlab_analysis_scripts/</code>.</p>
UWB ranging dataset collected in a Galvanic Plant
<p>Dataset collected as part of the following work:<br>A. Martinelli, S. Jayousi, S. Caputo and L. Mucchi, "UWB Positioning for Industrial Applications: the Galvanic Plating Case Study," <em>2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)</em>, Pisa, Italy, 2019, pp. 1-7, doi: 10.1109/IPIN.2019.8911746.</p>
UWB-IODA project: Datasets associated to WP3
<p>Measured IR-UWB data files corresponding to WP3 of the UWB-IODA project. The detailed description of the experimental setup and associated signal processing algorithms is provided at https://hal.archives-ouvertes.fr/hal-03582174.</p>
Signs of life detection behind obstacles using an UWB radar sensor
<p>The "Signs of life detection behind obstacles using an UWB radar sensor" dataset was created by University of West Attica, by collecting data from 9 people using an UWB sensor, which was the X4M200 UWB radar sensor by Novelda, with and without the existence of a wall between the subject and the radar for the purposes of RESCUER project (https://rescuerproject.eu/ - a Horizon 2020 Research & Innovation Programme under Grant Agreement No.101021836). In all cases, the subject was lying down in the same stance for about 1 min and the radar was placed in four different positions: a) 0.2 m from the ground, b) 0.5 m from the ground, c) 1 m from the ground, d) 1 m from the ground and with a 45 degree angle. For comparison purposes, we also include in the dataset the case where no human is present within the radar's detection area. The subjects were placed in a distance between 0.5 and 5 metres from the radar and different stances were considered, while data were collected both for the amplitude (designated as abs) and the phase of the signal (designated as angle). This information is designated in each filename. For example, the filename "2 facing radar abs.csv", indicates that the subject is placed in 2 metres away from the radar, he/she is facing the radar and the file contains the signal's amplitude. The sampling rate of the radar was set to 17 samples per second and a distance step of about 0.05144 m was also considered.</p>
AAU_Mehsed_UWB_Dataset
<p>We recorded data from a UAV and stationary anchors in an indoor environment. <br>UAV war equipped with a Pixhawk 4 flight controller and a companion computer (RPi4), an IMU, a barometer, and two UWB transceiver (Qorvo MDEK1001). <br>The UAV flew within a volume of roughly 5x8x8 m^3, covered by a motion capturing system, tracking the pose of the UAV at 60 Hz with sub-millimeter and sub-degree accuracy for ground truthing.<br>Nine stationary UWB nodes, each consisting of a UWB transceiver and a companion computer, were arbitrarily placed in the environment and their true positions was captured. <br>A SDS-TWR protocol with a fully meshed ranging scheme/cycling was used to measure the distance between any device. A full raning cycle takes roughly 1 second. <br>The net ranging rate is 100 Hz. The range measurements were streamed using the ROS1 middleware to the UAV to be recorded onboard in ROS1 bag files. The devices were synchronized with chrony.<br>During 12 flights, the UAV took off to 2m, executed three upward spirals reaching 8m, and landed at its initial position. <br>Anchors were randomly placed along the spiral flight path at heights ranging from 0.3m to 3.3m in an area of 5x8m^2. Each flight lasted around 140 seconds.<br><br>The spatial relationships, the sensor noise characteristics, and the pair-wise biases between the intial set of anchors and tags were determined with our Python range evaluation package (https://github.com/aau-cns/cnspy_ranging_evaluation).<br><br>Since the accuracy of TOA measurements is typically influenced by factors such as the spatial relationship between device antennas, pair-wise biases, and range-based biases, we substitute the actual range measurements of the 12 recorded flight datasets by the true ranges with Gaussian noise.<br>For these synthetic (hybrid) datasets, the zero-mean white Gaussian noise was applied with different standard deviations (0.01, 0.1, 0.2, 0.3) and without biases, and with a different percentage of outliers (1% and 10%) and a measurement noise of noise sigma = 0.1. <br>Note, that additional sequences can be created anytime, including biases, using the provided Python toolbox.</p> <p>Further details can be found in the README.md</p>
RUFF -- Rotating UWB For Fingerprint
<p>RUFF (Rotating UWB For Fingerprint) is a dataset for exploring the UWB device authentication through radio frequency fingerprinting.</p> <p>This dataset is composed of more then 1.5 million measurments of Chanel impulse Response (CIR) of UWB signal pulses. Each mesurment is labeled with a Device ID from 1 of 13 emitting boards and with a Device Location of 1 of 100 positions.</p> <p>The data is available in raw csv files from the recording campagn and in .npy clean format for a direct usage in python. The code and Deep Learning models from this project can be found in <a href="https://anonymous.4open.science/r/UWB-fingerprint-80CB/README.md" target="_blank" rel="noopener">This git</a>.</p> <p><em>Link to the Article with more detail will be added after review.</em></p>
UWB - BiRex
<p>**UWB** data were collected using **DWM3001CDK** development boards by Qorvo, for both **Channel 9 (8 GHz)** and **Channel 5 (6.5 GHz)**, in Line-of-Sight **(LOS)** as well as Non-Line-of-Sight **(NLOS)** conditions.</p> <p> The present dataset contains the following data for **UWB**:<br>* CIR real values;<br>* CIR imaginary values;<br>* Distance in centimeters;</p> <p>Additional Information:</p> <p># LOS/NLOS Observations</p> <p>| | R1 | R2 | R3 | R4 | R5 |<br>|----|----|----|----|----|----|<br>| L1 | X | | XX | | |<br>| L2 | | X | | X | X |<br>| L3 | | X | | | X |<br>| L4 | XX | X | | | |<br>| L5 | | | XX | | |</p> <p>X: **LOS** / XX: **Probably**</p> <p># Ground Truth</p> <p>| | R1 | R2 | R3 | R4 | R5 |<br>|----|----|----|----|----|----|<br>| L1 |400 |1279|544 |991 |1549|<br>| L2 |757 |916 |696 |567 |1284|<br>| L3 |1099|438 |1185|566 |1429|<br>| L4 |1352|1337|709 |417 |608 |<br>| L5 |1600|2003|694 |1114|762 |</p> <p> </p> <p><br>Please for more Information refer to the companion paper:<br>Nadir Bouzar, Luca De Nardis, Maria-Gabriella Di Benedetto, Enrico Maria Vitucci, Marco Chiani, Stefano Caputo, Lorenzo Mucchi, "IN-Rep: a new open data repository for AI-based positioning in Industrial Networks", IEEE 8th International Forum on Research and Technologies for Society and Industry (IEEE RTSI 2024) , Special Session 04 "Telecommunications Solutions for Next-Generation Industrial IoT (NG-IIoT)", September 18-20, 2024, Lecco, Italy.</p>
Dataset of UWB ranging measurements with smartphones
<p>We performed distance measurements with UWB smartphones in three different environments. Our results of the evaluation are part of the paper <em>Smartphones with UWB: Evaluating the Accuracy and Reliability of UWB Ranging</em> by Heinrich et al.</p>
A New Dataset of People Flow in an Industrial Site with UWB and Motion Capture Systems
<p>Improving performance and safety conditions at industrial sites remain key elements of the Group's strategy. Major issues require the ability to dynamically locate people and assets on the site. Currently, the security and regulation of access to areas with different characteristics (types of tasks, level of risk, or confidentiality...) are often carried out with badge doors or barriers. These means present several weaknesses in the face of inappropriate movements of people, but also of objects or tools. Also, there is increasing use of technological devices requiring precise localization in the industrial environment such as AGVs (mobile robots or drones) or augmented reality devices. It is therefore becoming essential to have tools to dynamically manage these flows of people or goods associated with precise location technologies. An Ultra-Wide-Band solution will be employed to quickly and efficiently identify persons who may find themselves in unauthorized areas or perform tasks for which they are uninstructed. Besides dynamic people tracking, this solution can overcome problems of moving objects and tools in production workshops. We propose a new dataset to have information about workers' displacement and evaluate workers' performance and safety.</p> <p>The dataset is available at this link: IndoorIndustrialLocalisationDataset: <a href="https://github.com/vauchey/IndoorInsdustrialLocalisationDataset/">https://github.com/vauchey/IndoorInsdustrialLocalisationDataset/</a></p> <p> </p> <p>Paper :</p> <p>Mickael Delamare<sup>1</sup>, Fabrice Duval<sup><span>2</span></sup>, Remi Boutteau<sup><span>3</span></sup>.</p> <p><sup>1</sup><a href="http://www.cesi.fr./">cesi</a>, CESI,Rouen campus, 76000 ROUEN, France,<a href="mailto:mdelamare@cesi.fr">mdelamare@cesi.fr</a><br> <span><sup>2</sup> </span><a href="https://lineact.cesi.fr/">LINEACT CESI</a>, LINEACT CESI, Rouen campus,76000 ROUEN, France, <a href="mailto:fduval@cesi.fr">fduval@cesi.fr</a><br> <span><sup>3</sup> </span><a href="http://www.esigelec.fr/">ESIGELEC</a> , IRSEEM, Rouen, France, Normandie Univ, UNIROUEN, <a href="mailto:remi.boutteau@univ-rouen.fr">remi.boutteau@univ-rouen.fr</a>,</p> <p>Link : <a href="https://www.mdpi.com/1424-8220/20/16/4511">https://www.mdpi.com/1424-8220/20/16/4511</a></p> <p>DOI : <a href="https://doi.org/10.3390/s20164511">https://doi.org/10.3390/s20164511</a></p> <p>Accepted to the journal Sensors (MDPI)</p> <p> </p>
Person detection using UWB and Monocular camera (With LiDAR ground-truth)
<p>This dataset was record using the ROS2-foxy framework and can be utilized with:</p> <pre><code>ros2 bag play square_test_with_gt</code></pre> <table> <tbody> <tr> <td>Name of ROS2 topic</td> <td>Type of ROS2 topic</td> <td>Information</td> </tr> <tr> <td>/Detections</td> <td>vision_msgs/msg/Detection2DArray</td> <td>This topic includes person detections from the monocular camera that is performing the Deep Learning object detection</td> </tr> <tr> <td>/GT_POINT</td> <td>geometry_msgs/msg/PointStamped</td> <td>Contains the PointStamped message obtained from the LiDAR person detection for ground truth purposes</td> </tr> <tr> <td>/distance_data_array</td> <td>itrci_hardware/msg/RadioRangeDataArray</td> <td>This topic has person detections from the 3 UWB Anchors relative to the person TAG (Note that is in custom ros2 message itrci_hardware)</td> </tr> <tr> <td>/tf</td> <td>tf2_msgs/msg/TFMessage</td> <td>base_link and odom tf (robot is static)</td> </tr> <tr> <td>/tf_static</td> <td>tf2_msgs/msg/TFMessage</td> <td>Contains tf information of LiDAR cameras and anchors relative to the robot base_link</td> </tr> </tbody> </table>
Person detection using UWB and Monocular camera (With LiDAR ground-truth) 0.7m/s
<p>This dataset was record using the ROS2-foxy framework and can be utilized with:</p> <pre><code>ros2 bag play square_test_with_gt_2 </code></pre> <table> <tbody> <tr> <td>Name of ROS2 topic</td> <td>Type of ROS2 topic</td> <td>Information</td> </tr> <tr> <td>/Detections</td> <td>vision_msgs/msg/Detection2DArray</td> <td>This topic includes person detections from the monocular camera that is performing the Deep Learning object detection</td> </tr> <tr> <td>/GT_POINT</td> <td>geometry_msgs/msg/PointStamped</td> <td>Contains the PointStamped message obtained from the LiDAR person detection for ground truth purposes</td> </tr> <tr> <td>/distance_data_array</td> <td>itrci_hardware/msg/RadioRangeDataArray</td> <td>This topic has person detections from the 3 UWB Anchors relative to the person TAG (Note that is in custom ros2 message itrci_hardware)</td> </tr> <tr> <td>/tf</td> <td>tf2_msgs/msg/TFMessage</td> <td>base_link and odom tf (robot is static)</td> </tr> <tr> <td>/tf_static</td> <td>tf2_msgs/msg/TFMessage</td> <td>Contains tf information of LiDAR cameras and anchors relative to the robot base_link</td> </tr> </tbody> </table>
Wavelet Domain Compensation of Frequency Dispersion of UWB Electromagnetic Waves for Time-Reversal Imaging (dataset)
<p>These files are the simulation data used to create the figures illustrated in the relevant journal paper. Each filename indicates which figure number it relates to. These files are text files. The first row in each file describes the content of each of its columns.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.