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- W4313144977 abstract "Radio tomographic imaging (RTI) has become a popular approach to reconstruct spatial loss fields (SLFs) in an area covered by a wireless network based on received signal strength (RSS) measurements. SLF images quantify the attenuation rate of the radio-frequency waves at each location in the network. The attenuation for the propagation path can be modeled as the 2-dimensional integral of SLF scaled by a weight function, which is the foundation of RTI techniques and makes the SLF reconstruction possible. In recent years, many methods, including machine-learning-based schemes, have been proposed to achieve more accurate SLF estimates. In this letter, we develop an attention neural network-augmented optimization SLF estimation scheme by taking advantage of deep learning and the traditional RTI technique. Our proposed method achieves the best reconstruction performance among the existing approaches." @default.
- W4313144977 created "2023-01-06" @default.
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- W4313144977 date "2022-01-01" @default.
- W4313144977 modified "2023-10-16" @default.
- W4313144977 title "Improving Radio Tomographic Imaging Accuracy by Attention Augmented Optimization Technique" @default.
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- W4313144977 doi "https://doi.org/10.1109/lsp.2022.3220149" @default.
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