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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Language: | English |
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IEEE
2019-01-01
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Series: | IEEE Access |
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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. |
first_indexed | 2024-12-22T21:55:04Z |
format | Article |
id | doaj.art-d3e64824775546a2b9a1a27f3624c4fa |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-12-22T21:55:04Z |
publishDate | 2019-01-01 |
publisher | IEEE |
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series | IEEE Access |
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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