Designing Multi-Task Convolutional Variational Autoencoder for Radio Tomographic Imaging

Radio tomographic imaging (RTI) emerges to model the environment and detect the passive targets by a wireless network. In this work, the received signal strength (RSS) measurements are collected from an uncalibrated network, and a multi-task convolutional variational autoencoder model is proposed to...

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Published in:IEEE transactions on circuits and systems. II, Express briefs Vol. 69; no. 1; pp. 219 - 223
Main Authors: Wu, Hongzhuang, Ma, Xiaoli, Liu, Songyong
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Attenuation
Calibration
Convolution
convolutional neural network
Image classification
Image reconstruction
multi-task learning
Noise level
Noise levels
Noise measurement
Radio tomographic imaging
Signal strength
Target detection
Tomography
variational autoencoder
Wireless networks
ISSN:1549-7747, 1558-3791
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Abstract Radio tomographic imaging (RTI) emerges to model the environment and detect the passive targets by a wireless network. In this work, the received signal strength (RSS) measurements are collected from an uncalibrated network, and a multi-task convolutional variational autoencoder model is proposed to realize RTI. The presented model is trained end-to-end to denoise the RSS measurements, reconstruct the static tomographic images, estimate the parameters of the wireless network, and classify the measurement noise level, simultaneously. The multi-task variational learning strategy is able to improve the generalization of the model. Numerical experiments demonstrate the efficacy of our RTI method.
AbstractList Radio tomographic imaging (RTI) emerges to model the environment and detect the passive targets by a wireless network. In this work, the received signal strength (RSS) measurements are collected from an uncalibrated network, and a multi-task convolutional variational autoencoder model is proposed to realize RTI. The presented model is trained end-to-end to denoise the RSS measurements, reconstruct the static tomographic images, estimate the parameters of the wireless network, and classify the measurement noise level, simultaneously. The multi-task variational learning strategy is able to improve the generalization of the model. Numerical experiments demonstrate the efficacy of our RTI method.
Author Ma, Xiaoli
Liu, Songyong
Wu, Hongzhuang
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SubjectTerms Attenuation
Calibration
Convolution
convolutional neural network
Image classification
Image reconstruction
multi-task learning
Noise level
Noise levels
Noise measurement
Radio tomographic imaging
Signal strength
Target detection
Tomography
variational autoencoder
Wireless networks
Title Designing Multi-Task Convolutional Variational Autoencoder for Radio Tomographic Imaging
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Volume 69
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