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A Data Preprocessing Method for Deep Learning-Based Device-Free Localization
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AI
DOI：10.1109/LCOMM.2021.3116150.png)
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摘要
En 中文
Deep learning models are widely used in the field of Device-Free Localization (DFL), and their performances rely heavily on the labeled dataset. In this letter, a novel data preprocessing method is proposed for exploiting the full potential of a collected dataset in DFL. Firstly, we propose to employ a pix-level multimodal representation of radio images for a fusion of both amplitude and phase features from Channel State Information (CSI). Then, a novel Conditional Generative Adversarial Network with Auxiliary Classifier (AC-GAN) model is used to generate artificial samples for further expanding the collected dataset. We further employ a regression formulation to train the Convolutional Neural Networks (CNNs) for positioning. The experimental results show that using the proposed method gains approximately 18% Root-Mean-Square-Error (RMSE) improvement over the CSI amplitude-only method and 22% over the CSI phase-only method.
Keyword:
Location awareness
Training
Data preprocessing
Generative adversarial networks
Convolutional neural networks
Generators
Transmitting antennas
Device-free localization
WiFi
fingerprint
channel state information (CSI)
generative adversarial network (GAN)
multimodal
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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