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Designing Multi-Task Convolutional Variational Autoencoder for Radio Tomographic Imaging
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DOI：10.1109/TCSII.2021.3081997.png)
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Abstract
En 中文
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.
Keywords:
Wireless networks
Image reconstruction
Noise level
Convolution
Calibration
Attenuation
Tomography
Radio tomographic imaging
variational autoencoder
convolutional neural network
multi-task learning
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