  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.
