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11 results for “ultra-wideband”
Dataset for "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices"
<p>Dataset for the paper "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices"</p> <p>The dataset contains localization measurements acquired with UWB devices. We compare the proposed localization method, called FlexTDOA, with a classic TDOA implementation, and with TWR-based localization. For more information about the localization methods, please refer to the paper.</p> <p>The dataset contains the measurements necessary to generate all the plots in the paper. For code examples on how to read and plot the data, please check out the associated Github repository: https://github.com/lauraflu/flextdoa</p> <p>If you find the dataset useful, please consider citing our work:</p> <blockquote> <p>Pătru, G. C., Flueratoru, L., Vasilescu, I., Niculescu, D., & Rosner, D. (2023). FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices. <em>IEEE Access</em>.</p> </blockquote>
Data from: Ultra-wideband and polarization-independent RCS reduction based on polarization conversion metasurface
<p>We proposed an ultra-wideband polarization conversion metasurface (PCM). Because it is an anisotropic structure with a pair of mutually perpendicular symmetric axes<i> u</i> and <i>v</i>, and the phase difference between the reflection coefficients under <i>u</i>- and <i>v</i>-polarized incidences is close to 180<sup>°</sup> in an ultra-wide frequency range from 9.5 to 29.5 GHz, it can realize ultra-wideband circular polarization (CP) maintaining reflection and make its co-polarized reflection coefficient under CP incidence close to 1.0 in the ultra-wide band 9.2-29.7 GHz. In addition, when its unit cell structure is rotated by an angle <i>ψ</i>, almost ±2<i>ψ</i> Pancharatnam-Berry (PB) phase will be generated in co-polarized reflection coefficient under CP incidence. Thus, based on the proposed PCM, an ultra-wideband Pancharatnam-Berry (PB) 2-dit coding metasurface is conveniently proposed. The simulation and experimental results show that the coding metasurface can achieve more than 10 dB radar cross section (RCS) reduction in the ultra-wide frequency band 8.7-29.3 GHz with a relative bandwidth of 108.4% under normal incidence with arbitrary polarizations, in addition, when the incident angle is increased to 30<sup>°</sup>, the RCS reduction performance can be kept well. In this data file, the simulation results obtained by commercial software Ansys HFSS and measurement results are listed.</p>
Dataset: Ultra-Wideband Ranging Measurements Acquired With Three Different Platforms (Qorvo, TDSR, 3db Access)
<p>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.</p> <p>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.</p> <p>The dataset (<strong>uwb_multiple_platforms.zip</strong>) contains the following directories:</p> <ul> <li><strong>parallel_measurements</strong> -- 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: <ul> <li><strong>[location_name] </strong> <ul> <li><strong>[LOSi/NLOSi]</strong> -- where i is the index of the recording and LOS/NLOS indicates whether that recording was acquired in LOS or NLOS <ul> <li><strong>info.csv</strong> -- 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.</li> <li><strong>unaligned_processed_data.csv</strong> -- 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.</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>split_train_test_val</strong> -- 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 <strong>parallel_measurements</strong>, 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.</li> <li><strong>trained_models_error_prediction</strong> -- Models trained for error prediction that were used to obtain the results from Section 4 from the paper.</li> </ul> <p>We also provide code examples for reading the data, training and testing the models, and analyzing the data at the following repository:</p> <p><a href="https://github.com/lauraflu/uwb-multiple-platforms">https://github.com/lauraflu/uwb-multiple-platforms</a></p> <p>The accompanying code is subject to change in the case of bugs/errors.</p> <p>For more information about how the measurements were acquired, please refer to the file <strong>documentation_dataset.pdf</strong>, which includes detailed information about each of the rooms, the device setup, the structure of the directories, etc.</p> <p>For any questions, do not hesitate to contact the authors of the paper.</p> <p><strong>Note</strong>: The dataset (in the <strong>parallel_measurements</strong> 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 <em>not</em> 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.</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>
Dataset: Indoor Localization with Narrow-band, Ultra-Wideband, and Motion Capture Systems
<p><strong>Localization Dataset README</strong></p> <p>In this, data for BLE and UWB calibration is included through the use of UWB and OptiTrack Motion capture system respectively. There are two sets of data each covering two scenarios; these being walking and trolley.</p> <p>The two groups containing the data are laid out identically as indicated below.</p> <p><strong>Bluetooth Low Energy / Ultra-Wideband</strong></p> <p>| Session ID | 8 x AoA | 8 x RSSI | BLE x | BLE y | UWB x | UWB y |</p> <p>Where :</p> <p>- AoA is Angle of Arrival, with two values given for each anchor node.<br> - RSSI is the Received Signal Strength Indicator, also with two values given for each anchor node.<br> - BLE x and y location estimates [1].<br> - UWB x and y location estimates.</p> <p><strong>Number of Samples (BLE/UWB)</strong></p> <p>The datasets contains the following number of samples:</p> <p>- Walk - 4896 samples.<br> - Trolley - 4856 samples.</p> <p><strong>Ultra-Wideband / OptiTrack Motion Capture</strong></p> <p>| Session ID | 4 x CIR | 4 x PSA | Distance | UWB x | UWB y | OPT x | OPT y |</p> <p>Where:</p> <p>- CIR is Channel Impulse Response for each received signal from the Anchors to the target tag.<br> - PSA is the Preamble Symbol Accumulation, from each of the 4 Anchors to the target tag.<br> - Distances of the tag to each of the 4 anchors.<br> - UWB x and y location estimates.<br> - OptiTrack location estimates.</p> <p><strong>Number of Samples (UWB/OPT)</strong></p> <p>- Walk - 2797 samples.<br> - Trolley - 3202 samples.</p> <p><strong>Total Number of Samples : 15751</strong></p> <p>[1]: A Khan, T Farnham, R Kou, U Raza, T Premalal, A Stanoev, W Thompson, "Standing on the Shoulders of Giants: AI-driven Calibration of Localisation Technologies", IEEE Global Communications Conference (GLOBECOM) 2019</p>
Data from: Ultra-wideband and polarization-independent RCS reduction based on polarization conversion metasurface
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Understanding and Mitigating the Impact of Wi-Fi 6E Interference on Ultra-Wideband Communications and Ranging
<p>Artifacts containing data sets and scripts for the paper "Understanding and Mitigating the Impact of Wi-Fi 6E Interference on Ultra-Wideband Communications and Ranging" published at the <em>International Conference on Information Processing in Sensor Networks </em>(IPSN) 2022.</p>
Personality and predictability in farmed calves using movement and space-use behaviours quantified by Ultra-wideband sensors
<p>Individuals within a population often show consistent between individual differences in their average behavioral expression (personality), and consistent differences in their within-individual variability of behavior around the mean (predictability). Where correlations between different personality traits and/or the predictability of traits exist, these represent behavioral or predictability syndromes. In wild populations, behavioral syndromes have consequences for individual's survival and reproduction and affect the structure and functioning of groups and populations. The consequences of behavioral syndromes for farm animals is less well explored, partly due to the challenges in quantifying behavior of many individuals across time and context in a farm setting. Here, we use Ultra-Wideband location sensors to provide precise measures of movement and space use for 60 calves over 40-48 days. We find that individuals show consistent within and between individual variation in movement and space use and show correlations in personality and predictability, indicating the existence of "exploratory" and "active" personality types in farmed calves. We consider the consequences of such individual variability for cattle behavior and welfare and how such data may be used to inform management decision in farm animals.</p>
Personality and predictability in farmed calves using movement and space-use behaviours quantified by Ultra-wideband sensors
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Ultra-wideband SAR Tomography on asteroids : FDBP and Compressive Sensing datasets
<p>Our knowledge of the internal structure of asteroids is currently indirect and relies on inferences from remote sensing observations of surfaces. However, it is fundamental for understanding small bodies’ history and for planetary defense missions. Radar observation of asteroids is the most mature technique available to characterize their inner structure, and Synthetic Aperture Radar Tomography (TomoSAR) allows 3D imaging by extending the synthetic aperture principle in the elevation direction. However, as the geometry of observation of small asteroids is complex, and TomoSAR studies have always been performed in the Earth observation geometry, TomoSAR results in a small body geometry must be simulated to assess the methods’ performances. Different tomography algorithms can be adopted, depending on the characteristics of the problem. While the Frequency Domain Back Projection (FDBP) is based on the correction of the Fourier transform of the received signal by an <em>ad-hoc</em> function built from the geometry of study, it can only retrieve the true position of the scatterers when applied along with ray-tracing methods, which are unreliable in the case of rough asteroid surfaces. Meanwhile, the Compressive Sensing (CS) is based on the compressive sampling theory, which relies on the hypothesis that few scatterers lie in the same direction from the subsurface. The CS can be used to retrieve the position of the scatterers, but its application in the small body geometry is questioned. Thus, both performances of the FDBP and the CS in a small body geometry are demonstrated, and the quality of the reconstruction is analyzed.</p>
UWB Ranging and Localization Dataset for "High-Accuracy Ranging and Localization with Ultra-Wideband Communication for Energy-Constrained Devices"
<pre><strong>UWB Ranging and Localization Dataset for "High-Accuracy Ranging and Localization with Ultra-Wideband Communication for Energy-Constrained Devices" </strong> This dataset accompanies the paper "<strong>High-Accuracy Ranging and Localization with Ultra-Wideband Communication for Energy-Constrained Devices</strong>," by <em>L. Flueratoru, S. Wehrli, M. Magno, S. Lohan, D. Niculescu</em>, accepted for publication in the IEEE Internet of Things Journal. Please refer to the paper for more information about analyzing the data. If you find this dataset useful, please consider citing our paper in your work. This dataset is split into two parts: "ranging" and "localization." Both parts contain measurements acquired with 3db Access and Decawave MDEK1001 UWB devices. In the "3db" and "decawave" datasets, when a recording has the same name, it means that the measurements were acquired at the exact same locations with the two types of devices. The "3db" ranging dataset contains, apart from these, more measurements acquired in various LOS and NLOS scenarios. In the directory "images" you can find photos of some of the setups. The "ranging" and "localization" directories both contain a "data" directory which holds the datasets and a "code" directory with Python scripts that show how to read and analyze the data. The 3db Access <em>ranging</em> recordings contain the following data: - True distance - Measured distance - Channel on which the measurements were acquired (can be 6.5, 7, or 7.5 GHz) - Time of arrival as identified by the chipset - Channel impulse response (CIR) - Line-of-sight (LOS)/non-line-of-sight (NLOS) scenario (encoded as 0 and 1, respectively) - If NLOS, the type of NLOS obstruction and its tickness. The Decawave <em>ranging</em> recordings contain the following data: - True distance - Measured distance - Line-of-sight (LOS)/non-line-of-sight (NLOS) scenario (encoded as 0 and 1, respectively) - If NLOS, the type of NLOS obstruction and its tickness. The MDEK kit operates only on the 6.5 GHz channel and cannot output the CIR without further code modifications, which is why this data is not available for the Decawave dataset. The <em>localization</em> dataset includes the following data: - True location as measured by an HTC Vive system - Estimated location using a Gauss-Newton trilateration algorithm (please refer to the paper for more details) - Distance measurements between each anchor and the tag. </pre>
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