A Highly Robust Indoor Location Algorithm Using WiFi Channel State Information Based on Transfer Learning Reinforcement

LI Yubai; SUN Xun

doi:10.11999/JEIT221160

Volume 45 Issue 10

Oct. 2023

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LI Yubai, SUN Xun. A Highly Robust Indoor Location Algorithm Using WiFi Channel State Information Based on Transfer Learning Reinforcement[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3657-3666. doi: 10.11999/JEIT221160

Citation:

LI Yubai, SUN Xun. A Highly Robust Indoor Location Algorithm Using WiFi Channel State Information Based on Transfer Learning Reinforcement[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3657-3666. doi: 10.11999/JEIT221160

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A Highly Robust Indoor Location Algorithm Using WiFi Channel State Information Based on Transfer Learning Reinforcement

doi: 10.11999/JEIT221160

LI Yubai,
SUN Xun^,

School of Information and Communication Engineering, University of Electronic Science and Technology, Chengdu 611731, China

Funds: Key R & D plan of Sichuan Province (23ZDYF0198)

Received Date: 2022-09-06
Rev Recd Date: 2023-02-14

Available Online: 2023-02-19

Publish Date: 2023-10-31

Abstract

Abstract

The WiFi fingerprint based on Channel State Information (CSI) data can be used for indoor positioning. Compared to Received Signal Strength Indicator (RSSI) data, CSI has a higher granularity of data information and can be obtained over multiple subcarriers. Better results can be achieved when using CSI data for indoor localization. However, regardless of whether RSSI or CSI signals are used, the indoor environment often changes after a period of time during the deployment of indoor localization, and the fingerprint database based on the test data often deteriorates or even becomes invalid. In this paper, using a transfer learning algorithm to establish a fingerprint database for indoor positioning is proposed. The advantage of transfer learning is that it can use less data to obtain better transfer training results. Transfer learning is used to migrate the prediction of fingerprint database, the life cycle of fingerprint database is prolonged, and robustness in indoor positioning is improved. The indoor positioning accuracy is maintained at 98% after one week and 97% after two weeks. At the same cost, the life cycle and positioning accuracy of the proposed model are higher than Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Machine (SVM), Deep Neural Networks (DNN), and other positioning systems.
- Wireless sensor networks,
- Indoor localization,
- Fingerprint localization,
- Channel State Information (CSI),
- Transfer learning

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References(33)

References

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