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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Veröffentlicht in:	IEEE transactions on circuits and systems. II, Express briefs Jg. 69; H. 1; S. 219 - 223
Hauptverfasser:	Wu, Hongzhuang, Ma, Xiaoli, Liu, Songyong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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