research-article

MAIL: Multi-Scale Attention-Guided Indoor Localization Using Geomagnetic Sequences

Authors:

Fan ZhouAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 2

Article No.: 54, Pages 1 - 23

https://doi.org/10.1145/3397335

Published: 15 June 2020 Publication History

Abstract

Knowing accurate indoor locations of pedestrians has great social and commercial values, such as pedestrian heatmapping and targeted advertising. Location estimation with sequential inputs (e.g., geomagnetic sequences) has received much attention lately, mainly because they enhance the localization accuracy with temporal correlations. Nevertheless, it is challenging to realize accurate localization with geomagnetic sequences due to environmental factors, such as non-uniform ferromagnetic disturbances. To address this, we propose MAIL, a multi-scale attention-guided indoor localization network, which turns these challenges into favorable advantages. Our key contributions are as follows. First, instead of extracting a single holistic feature from an input sequence directly, we design a scale-based feature extraction unit that takes variational anomalies at different scales into consideration. Second, we propose an attention generation scheme that identifies attention values for different scales. Rather than setting fixed numbers, MAIL learns them adaptively with the input sequence, thus increasing its adaptability and generality. Third, guided by attention values, we fuse multi-scale features by paying more attention to prominent ones and estimate current location with the fused feature. We evaluate the performance of MAIL in three different trial sites. Evaluation results show that MAIL reduces the mean localization error by more than 36% compared with the state-of-the-art competing schemes.

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Cited By

Wang QWang LFu MWang JSun LHuang RLi XJiang ZLuo H(2024)Multiscale Transformer and Attention Mechanism for Magnetic Spatiotemporal Sequence LocalizationIEEE Internet of Things Journal10.1109/JIOT.2024.336579311:11(19454-19469)Online publication date: 1-Jun-2024
https://doi.org/10.1109/JIOT.2024.3365793
Wang QJia JDeng YChen JWang XHuang MAghvami A(2024)DarLocExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122921244:COnline publication date: 15-Jun-2024
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Yan SSu YXiao JLuo XJi YGhazali K(2023)Deep Neural Network-Based Fusion Localization Using SmartphonesSensors10.3390/s2321868023:21(8680)Online publication date: 24-Oct-2023
https://doi.org/10.3390/s23218680
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Index Terms

MAIL: Multi-Scale Attention-Guided Indoor Localization Using Geomagnetic Sequences
1. Networks
  1. Network services
    1. Location based services

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 4, Issue 2

June 2020

771 pages

EISSN:2474-9567

DOI:10.1145/3406789

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Copyright © 2020 ACM.

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Published: 15 June 2020

Published in IMWUT Volume 4, Issue 2

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Cited By

Wang QWang LFu MWang JSun LHuang RLi XJiang ZLuo H(2024)Multiscale Transformer and Attention Mechanism for Magnetic Spatiotemporal Sequence LocalizationIEEE Internet of Things Journal10.1109/JIOT.2024.336579311:11(19454-19469)Online publication date: 1-Jun-2024
https://doi.org/10.1109/JIOT.2024.3365793
Wang QJia JDeng YChen JWang XHuang MAghvami A(2024)DarLocExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122921244:COnline publication date: 15-Jun-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122921
Yan SSu YXiao JLuo XJi YGhazali K(2023)Deep Neural Network-Based Fusion Localization Using SmartphonesSensors10.3390/s2321868023:21(8680)Online publication date: 24-Oct-2023
https://doi.org/10.3390/s23218680
Yan SSu YLuo XSun AJi YGhazali K(2023)Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead ReckoningRemote Sensing10.3390/rs1517416515:17(4165)Online publication date: 24-Aug-2023
https://doi.org/10.3390/rs15174165
Wang JZhao ZOu MCui JWu B(2023)Automatic Update for Wi-Fi Fingerprinting Indoor Localization via Multi-Target Domain AdaptationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962397:2(1-27)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3596239
He TNiu QLiu N(2023)GC-LocProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694956:4(1-27)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3569495
Elikplim NSilva BHancke GAbu-Mahfouz AShu L(2023)Wi-Fi Fine Time Measurement: Is it a Viable Alternative to Ultrawideband for Ranging in Industrial Environments?IEEE Industrial Electronics Magazine10.1109/MIE.2022.320543117:3(33-41)Online publication date: Sep-2023
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Shu MChen GZhang ZXu L(2023)Indoor Geomagnetic Positioning Using Direction-Aware Multiscale Recurrent Neural NetworksIEEE Sensors Journal10.1109/JSEN.2022.322795223:3(3321-3333)Online publication date: 1-Feb-2023
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Tiku SGufran DPasricha S(2023)Smartphone Invariant Indoor Localization Using Multi-head Attention Neural NetworkMachine Learning for Indoor Localization and Navigation10.1007/978-3-031-26712-3_14(337-355)Online publication date: 30-Jun-2023
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Guo BZuo WWang SLyu WHong ZDing YHe TZhang D(2022)WePosProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35345746:2(1-25)Online publication date: 7-Jul-2022
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