Tensor Sensing for RF Tomographic Imaging
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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, which helps exploit the geometric structures of the three-dimensional target and increases the recovery precision. In this paper, we propose a novel tensor sensing approach that achieves highly accurate estimation for real-world three-dimensional spaces. Firstly, we use the transform-based tensor model to formulate the RF tomographic imaging as a tensor sensing problem, and propose a fast alternating minimization algorithm called Alt-Min. Secondly, a detailed routine to carry out the algorithm is given, moreover, we propose a key optimization which resolves the huge memory consumption and long computation time problems. Finally, we present an 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.
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