Feature-Based Generalized Gaussian Distribution Method for NLoS Detection in Ultra-Wideband (UWB) Indoor Positioning System
نویسندگان
چکیده
Non-Line-of-Sight (NLoS) propagation condition is a crucial factor affecting the precision of localization in Ultra-Wideband (UWB) Indoor Positioning System (IPS). Numerous supervised Machine Learning (ML) approaches have been applied for NLoS identification to improve accuracy IPS. However, it difficult existing ML maintain high classification when database contains small number signals and large Line-of-Sight (LoS) signals. The inaccurate target node caused by this can still be problematic. To solve issue, we propose feature-based Gaussian Distribution (GD) Generalized (GGD) detection algorithms. By employing our algorithm imbalanced dataset, $96.7\%$ $98.0\%$ achieved. We also compared proposed with cutting-edge such as Support-Vector-Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Neural Network (NN), which achieve an $92.6\%$, $92.8\%$, $93.2\%$, $95.5\%$, respectively. results demonstrate that GGD dataset. Finally, higher different ratios LoS proves robustness effectiveness method.
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ژورنال
عنوان ژورنال: IEEE Sensors Journal
سال: 2022
ISSN: ['1558-1748', '1530-437X']
DOI: https://doi.org/10.1109/jsen.2022.3198680