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A Novel Device-Free Localization Approach Based on Deep Dictionary Learning

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Artificial Intelligence (CICAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13605))

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Abstract

As an emerging technology, device-free localization (DFL) has a wide range of application scenarios in the field of the internet of things. However, most of the existing DFL methods take the mode of learning features from raw data, and then perform to achieve localization using classification, which has inferior localization performance. To improve the localization accuracy, this study proposes an accurate and effective localization technique based on deep dictionary learning with sparse representation (DDL-DFL). The method extracts the in-depth features of the data through multi-layer dictionary learning and stacks the features of each layer for classification. Furthermore, we propose a data augmentation method, which can be applied to scenarios with fewer sensor nodes to increase the data dimension and strengthen the essential features to improve the accuracy of localization. We evaluate the performance of the DDL-DFL algorithm on collected laboratory datasets, and the results are superior to existing localization algorithms. In addition, the DDL-DFL algorithm with data augmentation is conducted on the laboratory datasets with a low dimension of data, and the localization performance has been significantly improved.

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Acknowledgements

This research was partially funded by the Guangxi Postdoctoral Special Foundation and the National Natural Science Foundation of China under Grants 61903090 and 62076077, respectively.

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Authors and Affiliations

  1. School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, 541004, China

    Manman Wang, Benying Tan, Shuxue Ding & Yujie Li

Authors
  1. Manman Wang
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  2. Benying Tan
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  3. Shuxue Ding
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  4. Yujie Li
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Corresponding author

Correspondence to Benying Tan .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Xiaomi Inc., Beijing, China

    Daniel Povey

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. JD Explore Academy, Beijing, China

    Tao Mei

  5. Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

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Wang, M., Tan, B., Ding, S., Li, Y. (2022). A Novel Device-Free Localization Approach Based on Deep Dictionary Learning. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_31

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  • DOI: https://doi.org/10.1007/978-3-031-20500-2_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20499-9

  • Online ISBN: 978-3-031-20500-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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