Conventional array radar systems require element spacing to be less than half a wavelength to mitigate grating lobes in forward-looking image, which results in a large number of elements and imposes a significant hardware burden. In this paper, an equivalent virtual uniform linear array is formulated with differential visualization technique and multiple snap-shot data. Then the regularized iterative adaptive approach (RIAA) is adopted to achieve superresolution imaging of the virtual linear array. Simulation results are given to illustrate the effectiveness of the proposed scheme.

To achieve high-quality forward-looking imaging of multi-channel radar, the fusion of synthetic aperture imaging result and real aperture superresolution result is usually necessary. However, the fusion processing by directly multiplying two results will seriously affect the image quality. In this paper, based on the analysis of the characteristics of synthetic aperture imaging results and super-resolution results, the logarithm transformation is introduced firstly on the synthetic aperture result before multiplication fusion processing to improve the image quality. Simulation results are illustrated to verify the effectiveness of the proposed scheme.

Multichannel radar has the potential of forward-looking imaging, but its azimuth resolution is usually poor due to the restriction of the platform size. Many superresolution methods have been developed to improve its azimuth resolution and sparse Bayesian learning (SBL)-based methods are popular within them. However, traditional SBL methods suffer from the over-sparse problem for extended targets, and they always fails to achieve good performance when there are both point targets and extended targets. In this paper, by judging the types of targets to assign different weights for different targets, and then applying the weighted average to the update results of hyperparameters in SBL iterations, a modified SBL-based scheme of multichannel radar forward looking imaging is proposed, and simulation results are illustrated to verify its effectiveness.

With the echo difference between different channels, the left/right ambiguity in single-channel forward-looking synthetic aperture radar (SAR) can be resolved using dual-channel radar. However, due to the restricted channel resources, existing methods have limited performance in resolving left/right ambiguity, especially in the area along the flight path. In this article, a novel left/right ambiguity resolving scheme is proposed to improve the imaging performance of dual-channel forward-looking SAR. In the scheme, the steering matrix considering the time-frequency–space information of echo was designed first, and the linear mapping equation between the echo and the original scene was established. Then, the regularized iterative adaptive approach (RIAA) is introduced to solve the equation, thereby achieving dual-channel forward-looking SAR left/right ambiguity resolving and superresolution imaging simultaneously and directly in the echo domain. At last, the simulated and experimental results were illustrated to demonstrate the effectiveness of the proposed scheme.

With multiple channels receiving echoes in azimuth, multichannel radar has the potential of forward looking high resolution imaging. However, due to the limitation of platform size, its azimuth resolution is poor. In this paper, by considering the effect of platform motion and off-grid problem, a multichannel radar forward looking superresolution imaging method based on atomic norm minimization is proposed. Simulation results are illustrated to verify the effectiveness of the method.

Multichannel radar has the potential of forward-looking imaging, but its azimuth resolution is usually poor due to the restriction of the platform size. Many superresolution methods have been developed to improve its azimuth resolution. However, these methods always have the problem of grid mismatch. In this paper, sparse Bayesian learning (SBL)-based multichannel radar forward-looking super-resolution imaging considering off-grid error is proposed, and simulation results are illustrated to verify its effectiveness.

To obtain high-resolution images of the objects in front of platform, a super-resolution scheme for multichannel radar forward-looking imaging considering failure channels and motion error is proposed in this study. In the scheme, a failure channel detection method based on the correlation of pulse-compressed data of different channels is presented first, and then a revised steering matrix considering failure channels and motion error is constructed. Finally, the echo data are processed by the iterative adaptive approach (IAA) with the revised steering matrix. Simulation results are given to illustrate the effectiveness of the proposed scheme when dealing with failure channels and motion error.