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 CS iterative optimization procedures. 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 ωK based CS and the traditional Fourier-based imaging techniques.

A novel two-dimensional (2-D) compressive sensing (CS) based method is presented for near-field radar imaging. First, an accurate near-field approximation is proposed, based on which the circular wavefront curvature of spherical waves can be compensated by mapping the images to a rectified new grid. More importantly, the near-field approximation makes the two dimensions of the scattered data separable for the range and cross-range directions, which makes it possible to solve the 2-D reflectivity matrix for the image reconstruction directly. Then, a 2-D proximal subgradient algorithm for near-field radar imaging based on a fast iterative shrinkage/thresholding algorithm (FISTA) is introduced to resolve the memory usage and computation time issues. Simulation and experimental results are provided to demonstrate the performance of the proposed method with comparisons to the traditional Fourier-based method and to the conjugate gradient (CG) based method, which proves that the proposed method is an effective way to solve the near-field radar imaging problem.

Microwave/millimeter-wave imaging has a wide prospect in applications of concealed weapons detection. The main problem of the existing techniques lies in the contradiction between the imaging speed and system cost. In this respect, a cylindrical sparse MIMO array topology is proposed, which has no mechanical scanning so as to achieve fast sampling process. Further, it can provide more angular observations than the mainly used systems with a planar aperture. To speed up the image reconstruction process, a phase center approximationbased transformation is made to convert the multistatic sampling scheme to its monostatic counterpart for which there are more fast and tractable imaging algorithms. Then, a phase calibration method is presented after we analyze the error of such an equivalence. In so doing, the cylindrical MIMO topology is transformed into an approximate monostatic one. Finally, a wavenumber domain algorithm is employed to attain the fast image reconstruction. Numerical simulations are provided to demonstrate the performance improvement of the proposed technique.

A novel 2D convex optimization-based compressive sensing (CS) method is presented for inverse synthetic aperture radar (ISAR) imaging. The method deals directly with the 2D signal model for the image reconstruction based on solving a convex optimization problem. The superiority of this method is that the memory usage and the computational complexity are much lower than those of the 1D CS-based ISAR imaging method. Especially, the method belongs to a convex optimization problem. Convex functions do not suffer from local minima assuring that the achieved solution always happens to be the optimal. Simulation and experimental results are provided to demonstrate the performance of the proposed method with comparisons to the traditional Fourier-based method and to the smoothed L0-norm-based method, which proves that the proposed method is an effective way to solve the ISAR imaging problem.

Multiple-input–multiple-output (MIMO) array-based millimeter-wave (MMW) imaging has a tangible prospect in applications of concealed weapons detection. A near-field imaging algorithm based on wavenumber domain processing is proposed for a cylindrical MIMO array scheme with uniformly spaced transmit and receive antennas over both the vertical and horizontal-arc directions. The spectrum aliasing associated with the proposed MIMO array is analyzed through a zero-filling discrete-time Fourier transform. The analysis shows that an undersampled array can be used in recovering the MMW image by a wavenumber domain algorithm. The requirements for the antenna interelement spacing of the MIMO array are delineated. Numerical simulations as well as comparisons with the backprojection algorithm are provided to demonstrate the effectiveness of the proposed method.

In the area of near-field millimeter-wave imaging, the generalized sparse array synthesis (SAS) method is in great demand. The traditional methods usually employ the greedy algorithms, which may have the convergence problem. This paper proposes a convex optimization model for the multiple-input multiple-output (MIMO) array design based on the compressive sensing (CS) approach. We generate a block shaped reference pattern, to be used as an optimizing target. The pattern occupies the entire imaging area of interest in order to involve the effect of each pixel into the optimization model. In MIMO scenarios, we can fix the transmit subarray and synthesize the receive subarray, and vice versa, or doing the synthesis sequentially. The problems associated with focusing, sidelobes suppression, and grating lobes suppression of the synthesized array are examined in detail. The simulation results reveal that the proposed method can efficiently synthesize a sparse array within a significantly shorter time in comparison to state-of-the-art techniques. As a result, it can be applied to generate arrays with large apertures. Additionally, both numerical and experimental results demonstrate that the synthesized sparse array offers superior image quality when compared to both state-of-the-art and commonly used sparse arrays with an equivalent number of antenna elements.