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Feature-based generalized Gaussian distribution method for NLoS detection in Ultra-Wideband (UWB) indoor positioning system

(2022) IEEE SENSORS JOURNAL. 22(19). p.18726-18739
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Abstract
Nonline-of-sight (NLoS) propagation condition is a crucial factor affecting the precision of the localization in the ultra-wideband (UWB) indoor positioning system (IPS). Numerous supervised machine learning (ML) approaches have been applied for the NLoS identification to improve the accuracy of the IPS. However, it is difficult for existing ML approaches to maintain a high classification accuracy when the database contains a small number of NLoS signals and a large number of line-of-sight (LoS) signals. The inaccurate localization of the target node caused by this small number of NLoS signals can still be problematic. To solve this issue, we propose feature-based Gaussian distribution (GD) and generalized GD (GGD) NLoS detection algorithms. By employing our detection algorithm for the imbalanced dataset, a classification accuracy of 96.7% and 98.0% can be achieved. We also compared the proposed algorithm with the existing cutting edge, such as support vector machine (SVM), decision tree (DT), naive Bayes (NB), and neural network (NN), which can achieve an accuracy of 92.6%, 92.8%, 93.2%, and 95.5%, respectively. The results demonstrate that the GGD algorithm can achieve high classification accuracy with the imbalanced dataset. Finally, the proposed algorithm can also achieve a higher classification accuracy for different ratios of LoS and NLoS signals, which proves the robustness and effectiveness of the proposed method.
Keywords
IDENTIFICATION, MITIGATION, MODEL, Gaussian distribution, IP networks, Support vector machines, Sensors, Location awareness, Ultra wideband technology, Training, Gaussian, distribution (GD) mixture models, generalized Gaussian distribution, (GGD), indoor positioning system (IPS), machine learning (ML), nonline-of-sight (NLoS) identification, ultra-wideband (UWB)

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Citation

Please use this url to cite or link to this publication:

MLA
Che, Fuhu, et al. “Feature-Based Generalized Gaussian Distribution Method for NLoS Detection in Ultra-Wideband (UWB) Indoor Positioning System.” IEEE SENSORS JOURNAL, vol. 22, no. 19, 2022, pp. 18726–39, doi:10.1109/JSEN.2022.3198680.
APA
Che, F., Ahmed, Q. Z., Fontaine, J., Van Herbruggen, B., Shahid, A., De Poorter, E., & Lazaridis, Pavlos, I. (2022). Feature-based generalized Gaussian distribution method for NLoS detection in Ultra-Wideband (UWB) indoor positioning system. IEEE SENSORS JOURNAL, 22(19), 18726–18739. https://doi.org/10.1109/JSEN.2022.3198680
Chicago author-date
Che, Fuhu, Qasim Zeeshan Ahmed, Jaron Fontaine, Ben Van Herbruggen, Adnan Shahid, Eli De Poorter, and I Lazaridis, Pavlos. 2022. “Feature-Based Generalized Gaussian Distribution Method for NLoS Detection in Ultra-Wideband (UWB) Indoor Positioning System.” IEEE SENSORS JOURNAL 22 (19): 18726–39. https://doi.org/10.1109/JSEN.2022.3198680.
Chicago author-date (all authors)
Che, Fuhu, Qasim Zeeshan Ahmed, Jaron Fontaine, Ben Van Herbruggen, Adnan Shahid, Eli De Poorter, and I Lazaridis, Pavlos. 2022. “Feature-Based Generalized Gaussian Distribution Method for NLoS Detection in Ultra-Wideband (UWB) Indoor Positioning System.” IEEE SENSORS JOURNAL 22 (19): 18726–18739. doi:10.1109/JSEN.2022.3198680.
Vancouver
1.
Che F, Ahmed QZ, Fontaine J, Van Herbruggen B, Shahid A, De Poorter E, et al. Feature-based generalized Gaussian distribution method for NLoS detection in Ultra-Wideband (UWB) indoor positioning system. IEEE SENSORS JOURNAL. 2022;22(19):18726–39.
IEEE
[1]
F. Che et al., “Feature-based generalized Gaussian distribution method for NLoS detection in Ultra-Wideband (UWB) indoor positioning system,” IEEE SENSORS JOURNAL, vol. 22, no. 19, pp. 18726–18739, 2022.
@article{8770001,
  abstract     = {{Nonline-of-sight (NLoS) propagation condition is a crucial factor affecting the precision of the localization in the ultra-wideband (UWB) indoor positioning system (IPS). Numerous supervised machine learning (ML) approaches have been applied for the NLoS identification to improve the accuracy of the IPS. However, it is difficult for existing ML approaches to maintain a high classification accuracy when the database contains a small number of NLoS signals and a large number of line-of-sight (LoS) signals. The inaccurate localization of the target node caused by this small number of NLoS signals can still be problematic. To solve this issue, we propose feature-based Gaussian distribution (GD) and generalized GD (GGD) NLoS detection algorithms. By employing our detection algorithm for the imbalanced dataset, a classification accuracy of 96.7% and 98.0% can be achieved. We also compared the proposed algorithm with the existing cutting edge, such as support vector machine (SVM), decision tree (DT), naive Bayes (NB), and neural network (NN), which can achieve an accuracy of 92.6%, 92.8%, 93.2%, and 95.5%, respectively. The results demonstrate that the GGD algorithm can achieve high classification accuracy with the imbalanced dataset. Finally, the proposed algorithm can also achieve a higher classification accuracy for different ratios of LoS and NLoS signals, which proves the robustness and effectiveness of the proposed method.}},
  author       = {{Che, Fuhu and Ahmed, Qasim Zeeshan and Fontaine, Jaron and Van Herbruggen, Ben and Shahid, Adnan and De Poorter, Eli and Lazaridis, Pavlos, I}},
  issn         = {{1530-437X}},
  journal      = {{IEEE SENSORS JOURNAL}},
  keywords     = {{IDENTIFICATION,MITIGATION,MODEL,Gaussian distribution,IP networks,Support vector machines,Sensors,Location awareness,Ultra wideband technology,Training,Gaussian,distribution (GD) mixture models,generalized Gaussian distribution,(GGD),indoor positioning system (IPS),machine learning (ML),nonline-of-sight (NLoS) identification,ultra-wideband (UWB)}},
  language     = {{eng}},
  number       = {{19}},
  pages        = {{18726--18739}},
  title        = {{Feature-based generalized Gaussian distribution method for NLoS detection in Ultra-Wideband (UWB) indoor positioning system}},
  url          = {{http://doi.org/10.1109/JSEN.2022.3198680}},
  volume       = {{22}},
  year         = {{2022}},
}

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