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.

In this Letter, the authors propose a novel framework based on block sparse Bayesian learning (bSBL) for exploiting the tree structure on wavelet coefficients in the process of recovering signals. A Boolean matrix is designed to transfer the tree structure of wavelet coefficients to a non-overlapped block structure. In this block-structured sparse model, the bSBL-based algorithm is used to learn the intra-block correlations and to derive the updating rule of model parameters. Experimental results show that for both 1D and 2D signals their proposed algorithm has superior performances compared with other model-based compressive sensing algorithms.

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