A Fast Imbalanced Binary Classification Approach to NLOS Identification in UWB Positioning
Joint Authors
Song, Bo
Li, Sheng-Lin
Tan, Mian
Ren, Qing-Hui
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-02
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Non-line-of-sight (NLOS) propagation is an important factor affecting the positioning accuracy of ultra-wide band (UWB).
In order to mitigate the NLOS ranging error caused by various obstacles in UWB ranging process, some scholars have applied machine learning methods such as support vector machine and support vector data description to the identification NLOS signals for mitigation NLOS error in recent years.
Therefore, the identification of NLOS signals is of great significance in UWB positioning.
The traditional machine learning method is based on the assumption that the number of samples of the line-of-sight (LOS) and NLOS signals are balanced.
However, in reality, the number of LOS signals in UWB positioning is much larger than the NLOS signals.
So the samples are characterized by class-imbalance.
In response to this fact, we applied a fast imbalanced binary classification method based on moments (MIBC) to identify NLOS signals.
The method uses the mean and covariance of the two first moments of the LOS signal samples to represent its probability distribution and then uses the probability distribution and all a small amount of NLOS signal samples to establish a model.
This method does not depend on the number of LOS signals and is suitable for dealing with the problem of classification of the imbalance between the number of LOS and NLOS signals.
Numerical simulations also verify that the method has better performance than LS-SVM and SVDD.
American Psychological Association (APA)
Song, Bo& Li, Sheng-Lin& Tan, Mian& Ren, Qing-Hui. 2018. A Fast Imbalanced Binary Classification Approach to NLOS Identification in UWB Positioning. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1205688
Modern Language Association (MLA)
Song, Bo…[et al.]. A Fast Imbalanced Binary Classification Approach to NLOS Identification in UWB Positioning. Mathematical Problems in Engineering No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1205688
American Medical Association (AMA)
Song, Bo& Li, Sheng-Lin& Tan, Mian& Ren, Qing-Hui. A Fast Imbalanced Binary Classification Approach to NLOS Identification in UWB Positioning. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1205688
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1205688