Compressive Sensing Imaging of 3-D Object by a Holographic Algorithm

TL;DR

This paper introduces a holographic algorithm-based compressive sensing method for 3-D millimeter-wave imaging that reduces computational complexity and improves image quality by avoiding large sensing matrices.

Contribution

It develops an interpolation-free holographic imaging algorithm as a sensing operator, eliminating the need for large-scale matrices in 3-D MMW compressive sensing.

Findings

Enhanced imaging speed and quality demonstrated in simulations.

Compared favorably against Omega-K CS and Fourier-based techniques.

Effective in near-field 3-D millimeter-wave imaging.

Abstract

Existing three-dimensional (3-D) compressive sensing-based millimeter-wave (MMW) imaging methods require a large-scale storage of the sensing matrix and immense computations owing to the high dimension matrix-vector model employed in the optimization. To overcome this shortcoming, we propose an efficient compressive sensing (CS) method based on a holographic algorithm for near-field 3-D MMW imaging. An interpolation-free holographic imaging algorithm is developed and used as a sensing operator, in lieu of the nominal sensing matrix typically used in the CS iterative optimization procedure. In so doing, the problem induced by the large-scale sensing matrix is avoided. With no interpolations required, both the computational speed and the image quality can be improved. Simulation and experimental results are provided to demonstrate the performance of the proposed method in comparison with those of the Omega-K based CS and the traditional Fourier-based imaging techniques.