Short-range millimeter-wave (MMW) multiple-input multiple-output synthetic aperture radar (MIMO-SAR) imaging technology has great development opportunities in the areas of security monitoring, medical diagnosis, and nondestructive evaluation. However, the existing MIMO-SAR fast imaging algorithms typically employ fast Fourier transform (FFT) to invert the target image, which has a serious influence on imaging quality under the highly displayed dynamic range. To address this deficiency, this article presents an adaptive alternating direction method of multipliers (ADMM)-based method on the basis of the state-of-the-art MIMO-SAR fast image-solving model. Different from the traditional ADMM, the proposed algorithm avoids the multiplication and inversion of a large-scale sensing matrix, and the computation is significantly reduced. In addition, adaptive weighting is introduced to adjust the intensity of the ${\ell _{1}}$ regularization item, with the objective of mitigating the loss in imaging accuracy caused by the state-of-the-art fast algorithm. On this foundation, considering the sensitivity of the optimized regularization coefficient to different targets and MIMO array configurations, an efficient optimized regularization coefficient searching method is developed, which greatly enhances the accomplishment of high-quality target image reconstruction in different scenarios as quickly as possible. Numerical simulations and experimental results validate the effectiveness of the proposed algorithm compared with those of the existing FFT-based algorithms.

In this letter, an efficient millimeter-wave (MMW) frequency-domain imaging algorithm is presented for 3-D object reconstruction of the near-field multiple-input–multiple-output synthetic aperture radar (MIMO-SAR) with nonuniform transmitting array. For each single-input–multiple-output (SIMO) case of the MIMO-SAR setup, the precise spectral echo expression is derived by spherical-wave decomposition and fast Fourier transform (FFT) operations. To obtain the linear phase in range direction, the multiorder approximation theory is employed to expand the coupling phases, and thus the target subimage can be reconstructed by utilizing phase compensation, central wavenumber approximation, and inverse FFT. Finally, the fully focused 3-D image for MIMO setup will be realized through accumulating all the SIMO subimages. Compared with the existing representative algorithms, our method avoids complicated interpolation and achieves high-quality fast imaging with only matrix multiplication and a small amount of iteration operations. The experimental results validate the effectiveness of the proposed algorithm.

In recent years, millimeter-wave (mmW) 3-D imaging technology for scanning 1-D single-input–multiple-output (SIMO) or multiple-input–multiple-output (MIMO) array has been extensively studied due to its unique advantages in near-field applications. However, current imaging algorithms for this kind of scheme are not satisfactory, either of low quality since irrational approximations are introduced or too slow as the back projection (BP) method is used. In this article, an efficient frequency-domain imaging algorithm is developed to fetch up these shortages. The precise spectral expression of the echo signal within SIMO data is derived through spherical wave decomposition and fast Fourier transform (FFT) operations. By properly approximating the nonlinear phase, the wavefront curvature is compensated after using a 1-D Stolt interpolation, and then the reflectivity map of the target can be obtained through several multiplications and inverse FFT operations. Image reconstruction for an MIMO array can be realized by coherently summing up all the SIMO focusing results. Both simulation analysis and experimental results demonstrate the effectiveness of the proposed algorithm in imaging quality and computational efficiency.

This paper presents a comprehensive measurement and calibration methodology for full-polarization characterization of radar targets at sub-terahertz (THz) frequencies. A novel three-object polarization calibration technique is proposed to address amplitude and phase imbalances in the measurement system. The method is implemented using a sub-THz radar cross-section measurement platform with twist waveguides for polarization switching. Experimental validation in an anechoic chamber demonstrates the method's effectiveness, achieving phase mismatch within 20°, polarization isolation better than 15 dB, and co-polarization amplitude imbalance below 2 dB. The calibrated data's accuracy is further confirmed through Cameron decomposition, showing excellent agreement with theoretical predictions. This work provides a robust framework for precise polarization characterization in the sub-THz regime, with potential applications in radar target recognition and imaging.

With the rapid development of terahertz radar technology, the electromagnetic device for terahertz radar cross-section (RCS) reduction is worth investigating. However, the existing research concentrates on the RCS reduction metasurface with fixed performance working in the microwave band. This paper proposes a terahertz dynamic coding metasurface integrated with vanadium dioxide (VO₂) for dual-polarized, dual-band, and wide-angle RCS reduction. The simulation result indicates that by switching the state of the VO₂ between insulator and metal, the metasurface can realize the effective RCS reduction at 0.18 THz to 0.24 THz and 0.21 THz to 0.39 THz under the left-handed and right-handed circularly polarized incident waves. When the polar and azimuth angles of the incident wave vary from 0° to 40° and 0° to 360° respectively, this metasurface can maintain a 10 dB RCS reduction. This work has potential value in the terahertz stealth field.

In this letter, an efficient near-field millimeter-wave (MMW) imaging algorithm is presented for layered dielectrics based on multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with nonuniform transmitting array. First, the model of target echo under MIMO-SAR configuration is built by dyadic Green's function, and the precise spectral echo expression is obtained through fast Fourier transform (FFT) and spherical-wave decomposition. Then the image reconstruction of sparse MIMO-SAR is divided into separate single-input multiple-output (SIMO)-SAR imaging problems, and the target's subimage corresponding to different SIMO-SAR setup can be quickly solved by employing inverse FFT, multi-step phase compensation and wavenumber integration. Finally, the fully focused MIMO-SAR reconstructed image will be obtained by coherently accumulating all the SIMO-SAR results. Simulation analysis and experimental results validate the effectiveness the proposed algorithm.