Multidimensional Data Tensor Sensing for RF Tomographic Imaging

TL;DR

This paper introduces a novel tensor sensing method using transform-based models and an efficient Alt-Min algorithm to improve RF tomographic imaging accuracy and speed in three-dimensional spaces.

Contribution

It proposes a new tensor sensing approach with an optimized Alt-Min algorithm for enhanced 3D RF imaging accuracy and computational efficiency.

Findings

Significant reduction in recovery error

Faster convergence compared to prior methods

Effective 3D space reconstruction using IKEA data

Abstract

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.