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 multipath-induced 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 target-induced 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.