A Data Augmentation-Based Method for Robust Device-Free Localization in Changing Environments of Passive Radio Frequency Identification System

Device-free localization (DFL) is playing a critical role in many applications which do not require any device attached to the target. Because of environmental changes, the fingerprint database established in the original environments is unable to remain effective when used in the changing environme...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 13
Main Authors: Ma, Yongtao, Ning, Wanru, Wang, Bobo, Liang, Xiuyan
Format: Journal Article
Language:English
Published: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Antenna measurements
Artificial neural networks
Changing environments
Convolutional neural network (CNN)
Data augmentation
denoising autoencoder (DAE)
device-free localization (DFL)
environmental changes
Feature extraction
Fingerprint recognition
Fingerprints
Identification systems
Localization
Location awareness
Noise reduction
Pollution measurement
Position measurement
Radio frequency
Radio frequency identification
Resampling
Signal to noise ratio
ultrahigh-frequency radio frequency identification (RFID) system
ISSN:0018-9456, 1557-9662
Online Access:Get full text
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Summary:Device-free localization (DFL) is playing a critical role in many applications which do not require any device attached to the target. Because of environmental changes, the fingerprint database established in the original environments is unable to remain effective when used in the changing environments. Looking at dropout as a prior-knowledge-free data augmentation operation, we propose a convolutional denoising autoencoder (DAE)-based DFL architecture AugRF. Our method combines the merits of the convolutional neural network and the DAE in which convolutional operation is beneficial to feature extraction and dropout operation is equivalent to generating augmented versions of the training data. It is noted that our method can improve the localization performance in changed environments without resampling and retraining processes. Simulations and real-world experiments verify the superiority of the proposed architecture. Our localization system can be implemented with a commercial ultrahigh-frequency radio frequency identification system. When the signal-to-noise ratio of the fingerprint data drops significantly due to environmental changes, AugRF contributes to improving localization accuracy. In addition, the time cost for one positioning meets the requirement of real-time indoor localization.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2021.3065426