To solve the problems of high computational complexity and unstable image quality inherent in the compressive sensing (CS) method, we propose a complex-valued fully convolutional neural network (CVFCNN)-based method for near-field enhanced millimeter-wave (MMW) three-dimensional (3-D) imaging. A generalized form of the complex parametric rectified linear unit (CPReLU) activation function with independent and learnable parameters is presented to improve the performance of CVFCNN. The CVFCNN structure is designed, and the formulas of the complex-valued back-propagation algorithm are derived in detail, in response to the lack of a machine learning library for a complex-valued neural network (CVNN). Compared with a real-valued fully convolutional neural network (RVFCNN), the proposed CVFCNN offers better performance while needing fewer parameters. In addition, it outperforms the CVFCNN that was used in radar imaging with different activation functions. Numerical simulations and experiments are provided to verify the efficacy of the proposed network, in comparison with state-of-the-art networks and the CS method for enhanced MMW imaging.

The wideband radar system is usually exploited to obtain the three-dimensional (3-D) image of the target under test. However, the wide bandwidth increases the system complexity and cost. Emerging techniques, such as the reconfigurable reflectarrays, reconstruct a near 3-D image via using single-frequency millimeter-wave (MMW) and near-field focusing of the beam, which greatly simplifies the system design. Whereas, the near-field focusing can only achieve a poor range resolution and a limited depth of focus. In this letter, we propose a multi-focus image fusion method based on the criterion of minimum entropy, which can further be utilized for any kind of MMW imaging system working at a single frequency. Simulations and experiments are presented to verify the effectiveness of the proposed method.

A novel dual-band chiral metamaterial (CMM) circular polarizer is proposed based on a dual-armed wheel-like structure. The proposed CMM can convert a linearly polarized electromagnetic wave into a circularly polarized (CP) mode. The simulation and measured results show, with good consistency, that the CMM exhibits good CP characteristics, such as a polarization isolation higher than 18 dB and an axial ratio lower than 1 dB. In addition, our studies show that all the resonance frequencies of this CMM structure are independent. Finally, a simple design method is presented and verified by a simulation to change the resonance frequencies of the proposed dual-band CMM circular polarizers.

Millimeter-wave (MMW) imaging has a tangible prospect in concealed weapon detection for security checks. Typically, a one-dimensional (1D) linear antenna array with mechanical scanning along a perpendicular direction is employed for MMW imaging. To achieve high-resolution imaging, the target under test needs to keep steady enough during the mechanical scanning process since slight movement can induce large phase variation for MMW systems, which will result in a blurred image. However, in the scenario of imaging of a human body, sometimes it is difficult to meet this requirement, especially for the elderly. Such blurred MMW images would reduce the detection accuracy of the concealed weapons. In this paper, we propose a deblurring method based on cycle-consistent adversarial network (Cycle GAN). Specifically, the Cycle GAN can learn the mapping between the blurred MMW images and the focused ones. To minimize the effect of the shaking blur, we introduce an identity loss. Moreover, a mean squared error loss (MSE loss) is utilized to stabilize the training, so as to obtain more refined deblurred results. The experimental results demonstrate that the proposed method can efficiently suppress the blurring effect in the MMW image.

Millimeter-wave imaging techniques have been used for security inspection at important checkpoints. With the increase of working frequencies of the imaging systems, the precise multichannel phase calibration becomes more and more important. Most of the traditional algorithms are used for the calibration of passive receiving antenna array, based on the signal model of far-field plane wave. However, the near-field spherical wave model should be utilized for the active wideband near-field imaging applications. Consequently, the traditional calibration algorithms cannot be used in wideband near-field imaging systems. The metal plate is usually employed for the calibration of wideband near-field imaging systems, but the precision is very sensitive to the flatness of the surface of the metal plate, which will result in additional phase error after calibration. In this letter, a multichannel phase error estimation and calibration method is proposed for the wideband linear array system. By using an extended ωK algorithm and the minimum-entropy method, the additional phase error of each channel after preliminary calibration is estimated and calibrated. Simulations and experimental results show the effectiveness of the proposed method.

Sidelobe level is a key parameter for phased array design. However, the conventional method for sidelobe level calculation is time-consuming due to the high sampling number of the radiation pattern. In this paper, an iterative sidelobe-seeking method for sidelobe calculation of arbitrarily distributed planar arrays is proposed to shorten the computing time. By applying Newton’s iterative method with an approximate formula, the proposed method uses less sampling to obtain the sidelobe positions and the peak sidelobe level. Numerical experiments were carried out and verified the effectiveness of the proposed method. Compared with the conventional method, the proposed method had a similar optimization effect but saved 80% of the algorithm time in the synthesis of a sparse planar array.