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Non-line-of-sight (NLOS) imaging is a novel radar sensing technology that enables the reconstruction of hidden targets. However, it may suffer from synthetic aperture length reduction caused by ambient occlusion. In this study, a complex total variation (CTV) regularization-based sparse reconstruction method for NLOS three-dimensional (3-D) imaging by millimeter-wave (mmW) radar, named RM-CSTV method, is proposed to improve imaging quality and speed. In this scheme, the NLOS imaging model is first introduced, and associated geometric constraints for NLOS objects are established. Second, an effective high-resolution NLOS imaging method based on the range migration (RM) kernel and complex sparse joint total variation constraint, dubbed as modified RM-CSTV, is proposed for 3-D high-resolution imaging with edge information. The experiments with multi-type NLOS targets show that the proposed RM-CSTV method can provide effective and high-resolution NLOS targets 3-D imaging.

To address the urgent need to improve the ship type discrimination capability in maritime target detection, this article explores synthetic aperture radar (SAR) three-dimensional (3-D) imaging of moving ships, which can provide high robustness and stability of 3-D structural characteristics for ship type discrimination. This paper defines the three-degree-of-freedom (3-DOF) micro-Doppler frequency of ship scatterers, specifically referring to the Doppler frequency caused by the three-axis rotation of the ship when the motion of the radar platform and the ship's planar motion are both compensated. The 3-DOF micro-Doppler frequency of the ship scatterers is closely related to the roll, pitch, and yaw three-axis rotation parameters of the ship and contains the 3-D coordinate information of the scatterers. Building upon the high-precision extraction of the 3-DOF micro-Doppler frequency of ship scatterers, this paper proposes an SAR 3-D imaging method for moving ships based on this frequency. The proposed method describes the ship's rotational characteristics using 27 parameters and converts the variation in the 3-DOF micro-Doppler frequency of the scatterer into a set of weighted coefficients of B-spline basis functions. Two long short-term memory networks are constructed to estimate the 27 parameters describing the ship's rotation and invert the 3-D coordinates of the scatterers. Finally, through the processing of simulated and experimental data, the feasibility of using the 3-DOF micro-Doppler frequency of ship scatterers to achieve high-precision estimation of the ship's three-axis rotation parameters and high-precision inversion of 3-D coordinates was verified.

In this article, to address the long-distance imaging problems, a novel forward-looking 3-D imaging method based on terahertz single-photon radar is presented. The imaging method is based on single-input and single-output architecture system and adopts a novel data-driven approach named scaling training. In the scaling training, by scaling the parameters of the long-distance scene to be imaged, a parameter scaling system is constructed, and then it is utilized to collect the training data to train the artificial neural network (ANN) model. Once the training is completed, 3-D image of the long-distance scene can be retrieved using the ANN model from solely the 1-D photon-counting histogram echo recorded by terahertz single-photon radar. On the basis, to further improve the imaging quality, the depth correction method is implemented. Finally, numerical simulations are carried out to demonstrate the feasibility of the proposed method.

Since its unique advantages in the tradeoff between data acquisition rate and hardware cost, the scanning 1-D multiple-input-multiple-output (MIMO) array setup has been widely used in nondestructive testing applications, especially for product quality control on the assembly line. In this article, two fast fully focused algorithms based on scanning 1-D MIMO array are developed for imaging half-space medium structures. Both of the algorithms are derived under the half-space Green's function, and are implemented through fast Fourier transform (FFT) and phase compensation. The only difference between these two algorithms is that the matrix multiplication and nonuniform FFT are, respectively, employed to reconstruct the range profile. Simulation and experimental results verify the effectiveness of the proposed algorithms in computation efficiency and image reconstruction quality.

Streak Tube Imaging Lidar (STIL), with advantages of non-scanning working mode, small distortion, high image framing rate, high resolution in low contrast environment, compact structure, easy miniaturization and high reliability, has a wide range of applications in military, aerospace, space confrontation, attack and defense, and marine law enforcement. This article introduces the principle of single-slit and multi-slit streak tube imaging lidar. It also introduces a single-slit general streak camera that can be used for imaging lidar. In addition, a multi-slit miniaturized streak tube with a single-lens focusing system with a total length of about 200 mm has been designed. The results of the 3D electromagnetic simulation show that the effective photocathode area of this streak tube reaches 36 mm × 36 mm, the temporal resolution is better than 50 ps, the dynamic spatial resolution can reach 12 lp/mm, and the whole photocathode can accommodate at least 19 slits in the effective detection range. The streak tube has a meshless structure, which is highly reliable. The streak tube can be used to increase the field of view of the imaging lidar system, improve the reliability, and achieve system miniaturization.

Multiple-input multiple-output (MIMO) array synthetic aperture radar (SAR) can straightly obtain the 3-D imagery of the illuminated scene with the single-pass flight. Generally, the Rayleigh resolution of the elevation direction is unacceptable due to the length limitation of linear array. The super-resolution imaging algorithms within the compressive sensing (CS) framework have been extensively studied because of the essential spatial sparsity in the elevation direction. However, the super-resolution performance of the existing sparse reconstruction algorithms will deteriorate dramatically in the case of lower signal-to-noise ratio (SNR) level or a few antenna elements. To overcome this problem, a new super-resolution imaging structure based on CS and deep neural network (DNN) for MIMO array SAR is proposed in this article. In this new algorithm, the spatial filtering based on CS is first proposed to reserve the signals only impinging from the prespecified space subregions. Thereafter, a group of parallel end-to-end DNN regression models are designed for mapping the potential sparse recovery mathematical model and further locating the true scatterers in the elevation direction. Finally, extensive simulations and airborne MIMO array SAR experiments are investigated to validate that the proposed method can realize the state-of-the-art super-resolution imaging against other existing related methods.