  Non-Line-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 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 Gaussian Distribution (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.
