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Erschienen in:
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2024 | OriginalPaper | Buchkapitel

An Unsupervised Domain Adaptation-Based Device-Free Sensing Approach for Cross-Weather Target Recognition in Foliage Environments

verfasst von : Yi Zhong, Tianqi Bi, Ju Wang, Minglei You, Ting Jiang

Erschienen in: Communications, Signal Processing, and Systems

Verlag: Springer Nature Singapore

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Abstract

In recent years, the emerging technique of device-free sensing (DFS) has gained popularity for foliage penetration (FOPEN) target recognition. This popularity is primarily attributed to its inherent advantage of not requiring specialized sensing equipment beyond wireless transceivers. Concerning weather variations, DFS heavily relies on labeled data for model training, which necessitates the annotation of samples for each weather environment. However, this annotation process proves impractical for real-world applications, especially under adverse weather conditions. To address this issue, this paper presents an unsupervised domain adaptation (UDA)-based cross-weather FOPEN target recognition system (CW-FTRS). Experimental results validate that the proposed method achieves an average accuracy of over 72% in unseen weather conditions using only unlabeled data samples.

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Vorheriges Kapitel Performance Analysis of Doppler Shift Estimator Based on Level Crossing Rate
Nächstes Kapitel Research on the Application of Encoding Error Correction Technology in Cloud Computing Data Recovery
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Metadaten
Titel
An Unsupervised Domain Adaptation-Based Device-Free Sensing Approach for Cross-Weather Target Recognition in Foliage Environments
verfasst von
Yi Zhong
Tianqi Bi
Ju Wang
Minglei You
Ting Jiang
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Communications, Signal Processing, and Systems

Print ISBN: 978-981-9975-55-6

Electronic ISBN: 978-981-9975-02-0

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-99-7502-0_18

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