Radar 3-D forward-looking imaging has always been a difficult issue in the radar detection. In this letter, a forward-looking 3-D imaging method based on attribute scattering model (ASM) for extended targets is proposed. First, an imaging model based on point scattering model (PSM) with wavefront modulation technique is constructed to achieve 3-D forward-looking imaging. Second, considering the fact that PSM-based imaging model assumes that the target is composed of a set of discrete points, it is not suitable for reconstructing the structure feature of extended targets, i.e., line structure and surface structure. To extract more geometry information of the target, the ASM that includes point scatterers (PSs), line-segment scatterers (LSSs), and rectangular-plate scatterers (RPSs) is adapted to the 3-D imaging model. Solving the parameter sets of PSs, LSSs, and RPSs with the alternating direction method of multipliers (ADMMs) algorithm, the edge and surface structure of the extended target can be reconstructed. The simulation results based on electromagnetic (EM) calculation by FEKO verify the effectiveness of the proposed method.

The restricted physical antenna aperture usually degrades the acquisition of target images with high azimuth resolution in radar forward-looking sight. To make breakthrough on this problem, this article offers a forward-looking radar imaging methodology by the illumination beam design and virtual aperture processing, which can improve the azimuth resolution. First, the imaging model is established with respect to the observation scenario, and the echo characteristics of the forward-looking virtual array are analyzed. Second, compared with the back-projection (BP)-based algorithm, the polar format imaging (PFI) algorithm with higher efficiency is proposed to obtain the focused images of target in the forward-looking locations. Finally, comprehensive simulations and real measured experiments are performed to corroborate the effectiveness of the proposals and show the imaging performance with different influencing factors.

In this letter, a novel microwave coincidence imaging (MCI) approach is proposed based on a multiple-input single-output (MISO) radar system to deal with the low signal-to-noise ratio (SNR) scenarios and off-grid problem. First, in coherent-detecting part, a linear frequency modulated (LFM) signal is transmitted, and dechirping processes are conducted to enhance the SNR of echoes. Then, in incoherent-modulating part, the post random phase-shifting modulations are conducted on the echoes, hence the temporal–spatial orthogonal reference radiation field of MCI is constructed, which provides the potential information of super-resolution. Further, to solve the off-grid problem of target’s scatterers in MCI, a new projecting-residual-based selection criterion is also proposed, combined with the preexisting signal subspace matching (SSM) method. The proposed method could largely eliminate the off-grid errors while conduct a reference matrix selection procedure, hence the reconstruction accuracy and computational complexity can be much improved and reduced, respectively. Finally, the validity of the proposed method and the super-resolution ability of MCI are verified by experiments.

Radar forward-looking imaging has always been a difficult problem in the radar detection. Modulating the wavefront of radar transmitted signals provides a feasible method for radar forward-looking imaging. The existing high-resolution radar imaging algorithms assume that the target scattering coefficients are sparsely distributed. However, for complex targets, the scattering coefficients no longer satisfy the sparse prior. To solve the problems of forward-looking imaging for complex targets, in this letter, a sparse representation imaging method with dictionary learning is proposed. First, the principle and imaging model of microwave modulation are introduced to achieve radar forward-looking imaging. Second, the dictionary learning method is developed to learn adaptive transformation that exploit the edge features and structural information as well as provide a sparser presentation to further improve the quality of radar images. Third, the imaging performance for different types of complex targets under different signal-to-noise ratios are analyzed. The simulation results show that the proposed method can effectively reconstruct different types of complex targets.

In this letter, a novel microwave coincidence imaging (MCI) method based on the attributed scattering model (ASM) is proposed. Unlike the classical MCI model which assumes the target as a set of discrete scatterers, the ASM-based MCI equation contains three kinds of reference matrices corresponding to the point-scatterers (PSs), the line-segment-scatterers (LSSs) and the rectangular-plate-scatterers (RPSs), respectively. Hence the ASM-based MCI could resolve richer information about the object geometries. By solving the imaging equation via the alternating direction method of multipliers (ADMM) algorithm, the scattering coefficients will be obtained and the target can be reconstructed according to the presetting parameter sets. Meantime, the ASM-based MCI also earns the superresolution ability like the classical MCI, which is brought in by the temporal-spatial orthogonal radiation field. Simulations and experiment are carried out to demonstrate the performance and superresolution ability of proposed method. The ASM-based MCI makes contributions to the progress of radar forward-looking imaging theory and technology.

Microwave coincidence imaging (MCI) is a novel staring imaging technique with high resolution in azimuth. In MCI, the continuous imaging area is discretized into fine grids and the target-scattering centers are assumed to be exactly located at the centers of prediscretized grids. Recently, parametric methods are applied to MCI as target reconstruction algorithms with resolution enhancement and quality improvement. However, in practical applications, grid mismatch will severely degrade the imaging quality of parametric methods because the target-scattering centers will not totally locate at the grid centers no matter how fine the grids are. In this article, a reweighted-dynamic-grid-based MCI (RDG-MCI) method is proposed. In RDG-MCI, grids are evolving from coarse to dense iteratively rather than being fixed, and hence, off-grid errors can be eliminated gradually. Meanwhile, the reconstructed coefficients are used as weighting factors of grids in a form of weighting matrix in the next iteration and nonkey grids will be dropped out. Hence, the dynamic grids will be focused around the positions where target scatterers are most likely to exist. Furthermore, the matrix uncertain sparse Bayesian learning (MUSBL) algorithm is adopted to eliminate the residual off-grid errors. Finally, a preferable imaging result can be obtained based on the updated nonuniform grids. Also, the theoretical expected Cramér–Rao bound (ECRB) is also derived to evaluate the performance of the proposed method. The effectiveness of the proposed method, along with the super-resolution ability of MCI, is verified by simulations and outdoor experiments.

The electromagnetic (EM) vortex imaging has demonstrated superior performance in target detection and imaging with azimuthal super-resolution. However, the restricted elevation resolution degrades the acquisition of target spatial information, which limits the development of the radar imaging technology based on orbital angular momentum (OAM). This article offers a solution to achieve the 3-D EM vortex imaging, by effectively utilizing relative motion between radar and target in the line-of-sight (LOS) direction. First, the forward-looking radar imaging scenario is presented, the 3-D echo model is derived, and the characteristics are analyzed as well. Second, the imaging method, based on the back-projection (BP) and spectrum estimation method, is proposed to obtain the target’s 3-D focused image. Furthermore, the influence factors about the elevation resolution are analyzed by the point spread function (PSF). Finally, simulations are carried out to verify the effectiveness of the theoretical analyses.