非视线传播
计算机科学
支持向量机
稳健性(进化)
人工智能
朴素贝叶斯分类器
特征提取
高斯分布
模式识别(心理学)
特征(语言学)
算法
无线
电信
生物化学
化学
物理
语言学
哲学
量子力学
基因
作者
Fuhu Che,Qasim Zeeshan Ahmed,Jaron Fontaine,Ben Van Herbruggen,Adnan Shahid,Eli De Poorter,Pavlos I. Lazaridis
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2022-10-01
卷期号:22 (19): 18726-18739
被引量:21
标识
DOI:10.1109/jsen.2022.3198680
摘要
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), naïve 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.
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