[time]
2023-05-31T14:27:30+02:00

[track]
7

[team_name]
WSL Hanayng

[team_institution]
Hanyang University

[logolink]
https://www.dropbox.com/s/ynvc90m8jkxx01s/Logo.png?dl=0

[team_members]
Sunwoo Kim, Paulson Eberechukwu N, Minsoo Jeong, Hongseok Jung, Suah Park

[reference_person]
Sunwoo Kim

[reference_email]
remero@hanyang.ac.kr

[description_short]
The WSL team proposes a convolution neural network (CNN)-based stacked autoencoder approach for indoor localization that leverages Channel impulse responses (CIR) and Time-of-arrival (TOA) fingerprints. The proposed approach utilizes a stacked autoencoder network to mitigate the effects of noise and signal fluctuations arising from Non-Line-of-Sight (NLOS) scenarios. The network is trained on pairs of noisy and fluctuating signals and their corresponding clean versions. By minimizing the reconstruction error, the network learns to denoise and restore signals affected by NLOS. The trained stacked autoencoder network effectively reduces noise and mitigates signal fluctuations, enhancing the accuracy and reliability of indoor localization systems in challenging NLOS environments.

[description_long_link]
https://www.dropbox.com/s/yd4evmaorvajuex/IPIN competition application.pdf?dl=0

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true

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