Indoor Localization Method Based on Fingerprint Expansion and Deep Learning
Authors: 
Yun He, Licai Zhu, Yong Li, Hao YangAuthors Info & Claims
Yun He, Licai Zhu, Yong Li, Hao YangAuthors Info & ClaimsPages 675 - 679
Abstract
In the field of indoor positioning, maintaining accuracy and robustness has been challenging due to the complexity of environments and signal instability. To address this issue, this paper introduces the FE-DeepLoc system. Firstly, a fingerprint data augmentation method is proposed to increase training samples and enhance model robustness. Secondly, a stacked convolutional autoencoder model is employed to process fingerprint data and extract more discriminative features. Finally, a similarity score is introduced to estimate the nearest fingerprint location to the test data. The trained model is used in combination with the similarity score for positioning. Experiments conducted in large indoor environments demonstrate that this system not only outperforms traditional methods in terms of positioning accuracy but also exhibits a degree of stability, providing users with accurate and reliable positioning services.
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Index Terms
- Indoor Localization Method Based on Fingerprint Expansion and Deep Learning
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Published In
October 2023
1809 pages
ISBN:9798400708305
DOI:10.1145/3650400
Copyright © 2023 ACM.
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Association for Computing Machinery
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Published: 17 April 2024
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EITCE 2023
EITCE 2023: 2023 7th International Conference on Electronic Information Technology and Computer Engineering
October 20 - 22, 2023
Xiamen, China
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Overall Acceptance Rate 508 of 972 submissions, 52%
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