At present, millimeter wave radar imaging technology has become a recognized human security solution in the field. The millimeter wave radar imaging system can be used to detect a concealed object; multiple-input multiple-output radar antennas and synthetic aperture radar techniques are used to obtain the raw data. The analytical Fourier transform algorithm is used for image reconstruction. When imaging a target at 90 mm from radar, which belongs to the near field imaging scene, the image resolution can reach 1.90 mm in X-direction and 1.73 mm in Y-direction. Since the error caused by the distance between radar and target will lead to noise, the original reconstruction image is processed by gamma transform, which eliminates image noise, then the image is enhanced by linearly stretched transform to improve visual recognition, which lays a good foundation for supervised learning. In order to flexibly deploy the machine learning algorithm in various application scenarios, ShuffleNetV2, MobileNetV3 and GhostNet representative of lightweight convolutional neural networks with redefined convolution, branch structure and optimized network layer structure are used to distinguish multi-category SAR images. Through the fusion of squeeze-and-excitation and the selective kernel attention mechanism, more precise features are extracted for classification, the proposed GhostNet_SEResNet56 can realize the best classification accuracy of SAR images within limited resources, which prediction accuracy is 98.18% and the number of parameters is 0.45 M.

In this paper, a multiband noncontact temperature-measuring microwave radiometer system is developed. The system can passively receive the microwave signal of the core temperature field of the human body without removing the clothes of the measured person. In order to accurately measure the actual temperature of multilayer tissue in human core temperature field, four frequency bands of 4–6 GHz, 8–12 GHz, 12–16 GHz, and 14–18 GHz were selected for multifrequency design according to the internal tissue depth model of human body and the relationship between skin depth and electromagnetic frequency. Used to measure the actual temperature of human epidermis, dermis, and subcutaneous tissue, a small and highly directional multiband angular horn antenna was designed for the radiometer front end. After the error analysis of the full-power microwave radiometer, a novel hardware architecture of the microwave interferometric temperature-measuring radiometer is proposed, and it is proven that the novel interferometric microwave radiometer has less error uncertainty through theoretical deduction. The experimental results show that the maximum detection sensitivity of the novel interferometric microwave temperature-measuring radiometer is 215 mV/dBm, and the temperature sensitivity is 0.047 K/mV. Compared with the scheme of the full-power radiometer, the detection sensitivity is increased 7.45-fold, and the temperature sensitivity is increased 13.89-fold.

Precise control of a coupling effect is critical for space-limited communication system applications. Dielectric metamaterials, which derive their electromagnetic properties from subwavelength structures, have emerged as a promising way to tune coupling effect due to their complex resonant modes. However, it has not yet achieved quantitative control of coupling effects in metamaterial designs, which is important for advanced engineering applications such as artificial intelligence antenna and reconfigurable camouflage. In this paper, all dielectric left-handed metamaterial operating in THz band based on the deep coupling of higher mode Mie resonance is presented. Distinguished from those designs published before, this left-handed metamaterial is composed only by one same structure, not two separated parts providing negative permittivity and permeability, respectively. By utilizing different Mie resonant modes, negative permittivity and permeability are accomplished via high order Mie resonant modes and the coupling between cubes. This work opens a gate to highly control of electromagnetic wave in dielectric metamaterial by using the coupling between the unit cells.

Polarization has always been an important issue in modern communication systems, especially in sensitive measurements. Conventional polarization converters show limited applications due to their large size and narrow bandwidth. In this paper, we demonstrate an ultra-wide band, multifunctional, and highly efficient metamaterial-based polarization converter that is capable of converting a linearly polarized wave into its cross-polarized wave and circularly polarized wave over different frequency bands. The design principle is based on the field transformation theory and the anisotropic plate is made with high/low permittivity strip metamaterials. The simulation results show that the metamaterial-based polarization converter is able to achieve linear-to-linear conversion over 11.5–12.6 GHz, and linear-to-circular conversion over two frequency bands, 3.0–11.5 GHz and 12.6–17.0 GHz, with an average polarization conversion efficiency over 90%. The polarization converter proposed in this paper provides an important stepping stone for future communication systems’ polarization control and can also be extended to higher frequency bands.

For uncooled LWIR system function limitations and lack of real-time image processing problems. The design scheme of high performance uncooled LWIR image processing system based on UAV system platform is proposed. The solution uses TI's TMS320C6657 DSP and Altera's EP3C25F324 FPGA processor as the system computing core, makes the long wave infrared image acquisition, FPGA image preprocessing, DSP image processing into one. And designed a set of systems in line with the external needs of the communication program.