Radio Tomographic Imaging Based on Low-Rank and Sparse Decomposition

Abstract

Imaging artifacts induced by the multipath interference in Radio-Frequency sensing network usually significantly degrade the performance of Radio Tomographic Imaging (RTI) and thereby has become a major challenge in the Device-Free Localization (DFL). The multipath in the environment often invalidates the commonly used sparsity-regularized methods for RTI reconstruction because the sparse multipathinduced imaging artifacts may be misestimated as the sparse target-induced attenuation. To enhance the sensing ability of the target's effect in RTI, for the first time, this paper utilized the Low-Rank and Sparse Decomposition (LRSD) to cope with the multipath-induced imaging artifacts and infer the sparse targetinduced attenuation accurately. The experimental results demonstrated the significant advantages of the proposed LRSD method in the reconstructed RTI quality, DFL accuracy, and time complexity in comparison to the presenting commonly used methods, including Tikhonov Regularization and Bayesian Compressive Sensing (BCS). The above advantages make the proposed LRSD method highly expected to improve the RTI performance and also make it applicable for real-time DFL applications. INDEX TERMS Wireless sensing network, device-free localization, Radio Tomographic Imaging, compressive sensing, low-rank and sparse decomposition.

A Neural Network-Based Microwave Imaging Method for Object Localization

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Exploring the Laplace Prior in Radio Tomographic Imaging with Sparse Bayesian Learning towards the Robustness to Multipath Fading

“…The more cluttered the environment is, the more severely the multipath fading exhibits on the RSS measurement. If the unreliable RSS variation is used for image reconstruction, some artifacts will emerge in the shadowing image, which will deteriorate the localization accuracy [1,24,41].…”

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