Multidimensional Data Tensor Sensing for RF Tomographic Imaging
This work addresses the challenge of accurately inferring multi-dimensional physical spaces in RF tomography, which is incremental as it builds on existing tensor-based compressed sensing with optimizations.
The paper tackles the problem of low recovery precision in RF tomographic imaging by proposing a novel tensor sensing approach, achieving significant improvements in recovery error and convergence speed compared to prior methods, as demonstrated on IKEA 3D data.
Radio-frequency (RF) tomographic imaging is a promising technique for inferring multi-dimensional physical space by processing RF signals traversed across a region of interest. However, conventional RF tomography schemes are generally based on vector compressed sensing, which ignores the geometric structures of the target spaces and leads to low recovery precision. The recently proposed transform-based tensor model is more appropriate for sensory data processing, as it helps exploit the geometric structures of the three-dimensional target and improve the recovery precision. In this paper, we propose a novel tensor sensing approach that achieves highly accurate estimation for real-world three-dimensional spaces. First, we use the transform-based tensor model to formulate a tensor sensing problem, and propose a fast alternating minimization algorithm called Alt-Min. Secondly, we drive an algorithm which is optimized to reduce memory and computation requirements. Finally, we present evaluation of our Alt-Min approach using IKEA 3D data and demonstrate significant improvement in recovery error and convergence speed compared to prior tensor-based compressed sensing.