1.5-D Sparse Array for Millimeter-Wave Imaging Based on Compressive Sensing Techniques

Abstract

The goal of this paper is to reduce the antenna count in a millimeter (mm)-wave imaging system by proposing both hardware and software solutions. The concept of image sparsity in the transform domain is utilized to present the compressive sensing (CS) formulation for both mono-static and multistatic imaging at mm-wave frequencies. To reduce the complexity of the imaging system and reconstruction process, we introduce 1.5-D array structure, which is a random sparse array with orthogonal element locations. It is shown that the peak signal-to-noise ratio (PSNR) of the reconstructed image obtained by a 1.5-D array with 65% sparsity is very close to the PSNR of a uniform 2-D array for mono-static imaging. Moreover, for multistatic imaging, it is illustrated that a high-quality image is reconstructed with 92% reduction in antenna elements, which significantly reduces the cost, scan time, and system complexity. Furthermore, a 1.5-D array of 16 patch antennas with 60% sparsity, operating from 29 to 32 GHz is designed and implemented to investigate the performance of the proposed 1.5-D array for imaging, as well as the presented CS-based reconstruction method. Measurements show that the quality of the reconstructed image using this 1.5-D array with CS-based reconstruction is very close to a full 2-D array; for example, the PSNR achieved for the reconstructed image of a knife is 33 dB referenced to a uniform 2-D array. The proposed reconstruction method results in 6–11 dB improvement in PSNR compared with the conventional reconstruction methods with a reasonable increase in computing load.