Enhanced Three-dimensional Building Layout Tomographic Imaging via Tensor Approach

The pursuit of high-quality building layout images is a key objective in radio tomographic imaging (RTI) as it provides essential information for precise indoor target localization. This study addresses the challenge of tomographic imaging for three-dimensional (3D) building layout, introducing a tensor-based enhancement imaging method. Specifically, first, the linear tomographic model is built by considering the relationship between the time delay of the transmissive signal and unknown region. By solving the tomographic model, the initial spatial map can be derived, and it is characterized as a three-order tensor, encapsulating the spatial attributes of the building. In the proposed enhanced imaging method, it leverages the spatial correlations, smoothness, and adaptive group sparsity properties inherent in 3D building layouts, and embeds those prior knowledge into the tensor-based optimization framework, which not only enhances reconstruction accuracy but also suppresses the striping artifacts. Numerical simulations and experiments are conducted to validate the proposed algorithm, with comparisons made against state-of-the-art methods. The results consistently demonstrate a substantial improvement in the quality of building layout image, which underscores its high potential and applicability within the field of radio tomography.