CAEFI: Channel State Information Fingerprint Indoor Location Method Using Convolutional Autoencoder for Dimension Reduction

WANG Xudong; LIU Shuai; WU Nan

doi:10.11999/JEIT210663

Volume 44 Issue 8

Aug. 2022

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WANG Xudong, LIU Shuai, WU Nan. CAEFI: Channel State Information Fingerprint Indoor Location Method Using Convolutional Autoencoder for Dimension Reduction[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2757-2766. doi: 10.11999/JEIT210663

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WANG Xudong, LIU Shuai, WU Nan. CAEFI: Channel State Information Fingerprint Indoor Location Method Using Convolutional Autoencoder for Dimension Reduction[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2757-2766. doi: 10.11999/JEIT210663

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CAEFI: Channel State Information Fingerprint Indoor Location Method Using Convolutional Autoencoder for Dimension Reduction

doi: 10.11999/JEIT210663 cstr: 32379.14.JEIT210663

Information Science Technology College, Dalian Maritime University, Dalian 116000, China

Funds: The National Natural Science Foundation of China (61371091)

Received Date: 2021-07-02
Rev Recd Date: 2021-10-28

Available Online: 2021-11-10

Publish Date: 2022-08-17

Abstract

Abstract

In order to improve the performance of Wi-Fi fingerprint indoor positioning technology, a method based on Convolutional Neural Networks (CNN) for Channel State Information (CSI) fingerprint indoor positioning is first proposed. This method combines the CSI amplitude difference and phase difference information to train the CNN model in the offline stage. Positioning experiments are carried out in two different indoor positioning scenarios in the gallery and the laboratory, and the average positioning errors of 25 cm and 48 cm are obtained respectively; Then, on this basis, the focus is on improving the timeliness of CNN-based CSI indoor positioning. The Convolutional AutoEncoder (CAE) is introduced to realize the dimensionality reduction processing of CSI. Under the premise of ensuring the accuracy of the original positioning method, the positioning time is increased by 40% and the memory consumption is reduced to 1/15 of the original algorithm. The experimental results verify the effectiveness of the proposed algorithm.
- Indoor fingerprint positioning,
- Channel State Information(CSI),
- Convolutional Neural Network(CNN),
- Convolutional AutoEncoder(CAE)

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References(28)

References

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