Ultra WideBand (UWB) technology
In this dataset, a human detecting model using with UWB radar technology is presented. Two distinct datasets were created using the UWB radar device, leveraging its dual features. Data collection involved two main scenarios, each containing multiple sub-scenarios. These sub-scenarios varied parameters like the position, distance, angle, and orientation of the human subject relative to the radar. Unlike conventional approaches that rely on signal processing or noise/background removal, this study uniquely emphasizes analyzing raw UWB radar data directly.
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Ultra-wideband radar (UWB) is capable of perceiving the surroundings irrespective of the visibility due to its broad frequency spectrum. Therefore, UWB technology can be employed in mobile robots to perform simultaneous localization and mapping (SLAM) in vision-denied environments (e.g. smoke, fog, walls with reflective surfaces). We chose four different environments to teleoperate a TurtleBot2 nonholonomic robot equipped with Novelda X4M300 monostatic radar modules and RPLIDAR-A2 laser range scanner(s).
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This dataset is collected to benchmark different software algorithms for UWB indoor localization. The dataset is collected in a controlled environment with mm-accurate groundtruth to evaluate different algorithms. The data is collected with the Wi-Pos UWB system,a platform developed for research data collection with a wireless long-range Sub-GHz backbone combined with UWB ranging based on the DW1000. The UWB is configured with a center frequency of 6489.6 MHz, a bandwidth of 499.2 MHz (channel 5) and a preamble length of 1024 symbols.
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The accuracy and reliability of an Ultra- WideBand (UWB) Indoor Positioning System (IPS) are compromised owing to the positioning error caused by the Non-Line-of-Sight (NLoS) signals. To address this, Machine Learning (ML) has been employed to classify Line-of-Sight (LoS) and NLoS components. However, the performance of ML algorithms degrades due to the disproportion of the number of LoS and NLoS signal components. A Weighted Naive Bayes (WNB) algorithm is proposed in this paper to mitigate this issue.
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313 Views
Improving performance and safety conditions on industrial sites remains a key element of the company's strategy. The major challenges require, the ability to dynamically locate people and goods on the site. Security and regulation of access to areas with different characteristics (types of tasks, level of risk or confidentiality...) are often ensured by doors or badge barriers. These means have several weaknesses when faced with inappropriate movements of people, but also an inappropriate use of objects or tools.
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