  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.
