A fast imbalanced binary classification approach to NLOS identification in UWB positioning
type: Item
instance of: scholarly article
title: A fast imbalanced binary classification approach to NLOS identification in UWB positioning
zbMATH Open document ID: 1426.62390
DOI: 10.1155/2018/1580147
author: Bo Song
author: Sheng-Lin Li
author: Mian Tan
author: Qing-Hui Ren
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 62P30
Mathematics Subject Classification ID: 62H30
zbMATH DE Number: 7018504
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/1580147
OpenAlex ID: W2902108480
cites work: NLOS identification and weighted least-squares localization for UWB systems using multipath channel statistics
cites work: 10.1162/153244303321897726
description: scientific article; zbMATH DE number 7018504