A Novel Outlier Immune Multipath Fingerprinting Model for Indoor Single-Site Localization

Multipath fingerprinting is a promising indoor location technique, which contains abundant position features of the received array signals. Effectively representing the position information is one critical issue in fingerprinting localization. Meanwhile, positional accuracy is prone to reduction cau...

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Main Authors: Limin Chen, Xionghui Yang, Peter X. Liu, Chunquan Li
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8641266/
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author Limin Chen
Xionghui Yang
Peter X. Liu
Chunquan Li
author_facet Limin Chen
Xionghui Yang
Peter X. Liu
Chunquan Li
author_sort Limin Chen
collection DOAJ
description Multipath fingerprinting is a promising indoor location technique, which contains abundant position features of the received array signals. Effectively representing the position information is one critical issue in fingerprinting localization. Meanwhile, positional accuracy is prone to reduction caused by abnormal measurement readings, which are referred to as outliers, and it has received a little attention in the existing literature. A multipath fingerprinting model for indoor single-site localization is proposed. In this model, the location fingerprint is composed of the spatial-temporal covariance matrix of the multipath signals received by the base station antenna array. The low-dimensional linear subspace of the location fingerprinting is introduced as feature descriptors. Based on the fact that the Grassmann manifold maintains the orthogonality of the linear subspace, the Binet-Cauchy kernel is employed to map the multipath fingerprinting to a higher dimensional reproducing kernel Hilbert space. The Euclidean distance of the nearest point between multipath fingerprinting affine hulls is adopted to represent the similarity of the position. Moreover, an augmented Lagrangian and alternating direction solution is given to remove the influence of outliers. We extensively evaluated the proposed method with the indoor multi-scenario benchmark data set. All the results demonstrate that the location accuracy of the proposed positioning model outperforms the existing method in an indoor environment. As the proportion of outliers increases, the positional accuracy loss of the proposed model is negligible.
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spelling doaj.art-d3e64824775546a2b9a1a27f3624c4fa2022-12-21T18:11:16ZengIEEEIEEE Access2169-35362019-01-017219712198010.1109/ACCESS.2019.28991698641266A Novel Outlier Immune Multipath Fingerprinting Model for Indoor Single-Site LocalizationLimin Chen0https://orcid.org/0000-0001-8022-5565Xionghui Yang1https://orcid.org/0000-0002-5762-985XPeter X. Liu2Chunquan Li3https://orcid.org/0000-0002-5493-6379School of Mechatronics Engineering, Nanchang University, Nanchang, ChinaSchool of Information Engineering, Nanchang University, Nanchang, ChinaSchool of Information Engineering, Nanchang University, Nanchang, ChinaSchool of Information Engineering, Nanchang University, Nanchang, ChinaMultipath fingerprinting is a promising indoor location technique, which contains abundant position features of the received array signals. Effectively representing the position information is one critical issue in fingerprinting localization. Meanwhile, positional accuracy is prone to reduction caused by abnormal measurement readings, which are referred to as outliers, and it has received a little attention in the existing literature. A multipath fingerprinting model for indoor single-site localization is proposed. In this model, the location fingerprint is composed of the spatial-temporal covariance matrix of the multipath signals received by the base station antenna array. The low-dimensional linear subspace of the location fingerprinting is introduced as feature descriptors. Based on the fact that the Grassmann manifold maintains the orthogonality of the linear subspace, the Binet-Cauchy kernel is employed to map the multipath fingerprinting to a higher dimensional reproducing kernel Hilbert space. The Euclidean distance of the nearest point between multipath fingerprinting affine hulls is adopted to represent the similarity of the position. Moreover, an augmented Lagrangian and alternating direction solution is given to remove the influence of outliers. We extensively evaluated the proposed method with the indoor multi-scenario benchmark data set. All the results demonstrate that the location accuracy of the proposed positioning model outperforms the existing method in an indoor environment. As the proportion of outliers increases, the positional accuracy loss of the proposed model is negligible.https://ieeexplore.ieee.org/document/8641266/Indoor localizationfeature descriptoroutliermultipath fingerprinting model
spellingShingle Limin Chen
Xionghui Yang
Peter X. Liu
Chunquan Li
A Novel Outlier Immune Multipath Fingerprinting Model for Indoor Single-Site Localization
IEEE Access
Indoor localization
feature descriptor
outlier
multipath fingerprinting model
title A Novel Outlier Immune Multipath Fingerprinting Model for Indoor Single-Site Localization
title_full A Novel Outlier Immune Multipath Fingerprinting Model for Indoor Single-Site Localization
title_fullStr A Novel Outlier Immune Multipath Fingerprinting Model for Indoor Single-Site Localization
title_full_unstemmed A Novel Outlier Immune Multipath Fingerprinting Model for Indoor Single-Site Localization
title_short A Novel Outlier Immune Multipath Fingerprinting Model for Indoor Single-Site Localization
title_sort novel outlier immune multipath fingerprinting model for indoor single site localization
topic Indoor localization
feature descriptor
outlier
multipath fingerprinting model
url https://ieeexplore.ieee.org/document/8641266/
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