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Wireless Tomography in Noisy Environments Using Machine Learning
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DOI：10.1109/TGRS.2013.2245904.png)
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Abstract
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This paper, one in a continuing series, describes a new initiative in wireless tomography. Our goal is to combine two technologies: wireless communication and radio frequency tomography, for the close-in remote sensing. The hybrid system, including wireless communication devices for wireless tomography is proposed in this paper. Noise reduction, modified standard phase reconstruction, and imaging are exploited sequentially to perform wireless tomography in noisy environments. The performance given in this paper illustrates the significance and prospect of wireless tomography. The contributions of this paper are threefold: 1) the hybrid system provides a strong and flexible infrastructure for wireless tomography; 2) machine learning, especially nonlinear dimensionality reduction, is explored to execute noise reduction and combat the nonlinear noise effect; and 3) modified standard phase reconstruction is well achieved using the de-noised amplitude-only total fields from the simple sensors and the received accurate full-data total fields from the advanced sensors. Experimental data provided by the Institute Fresnel in Marseille, France are used to demonstrate the concept of wireless tomography and validate the corresponding algorithms.
Keywords:
Imaging
machine learning
noise reduction
phase reconstruction
wireless tomography
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