Ultra WideBand (UWB) technology
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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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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