With multiple channels receiving echoes in azimuth, multichannel SAR has the potential of forward-looking imaging. However, its angular resolution, especially the area along the platform flight path, is poor due to the small variation of viewing angle. In this paper, the scheme of angular resolution enhancement for multichannel forward-looking SAR imaging based on zero-shot learning is proposed. In the scheme, the preliminary imaging results are obtained firstly with the synthetic aperture processing, and then the results are divided into three equal parts in azimuth, namely left, middle and right parts, to construct the dataset. Then by incorporating the frequency domain loss and mixed attention module, the modified CycleGAN network is developed to learning the mapping between the middle part (the area with lower resolution) and the left/right parts (the area with higher resolution). At last, experimental results are illustrated to verify that the proposed scheme can enhance the angular resolution of the middle area to the level of left/right area for the forward-looking images even without ground truths.

Multichannel synthetic aperture radar (SAR) has the potential of resolving left/right ambiguity and then achieving high-resolution forward-looking imaging. However, when the sparse channel configuration is adopted, there are always residual grating lobes and left/right ambiguity in the imaging results. In this paper, a scheme of simultaneously suppressing residual grating lobes and left/right ambiguity for sparse channel forward-looking SAR imaging is proposed. Firstly, the formation of the residual grating lobes and left/right ambiguity are analyzed based on the signal model and imaging procedure of multichannel forward-looking SAR. Then, the characteristics of the differences in the position of grating lobes for the imaging results of different snapshots, and the spatiotemporal coupling characteristics of suppression of grating lobes and left/right ambiguity are explained. Next, a space-time steering matrix based on range history information is constructed, which establishes a linear mapping relationship between the space-time echo and the original scene. By solving the linear equation with a regularized iterative adaptive approach, the residual grating lobes and left/right ambiguity are suppressed simultaneously. The extensive simulation and experimental results demonstrate the effectiveness of the proposed method.

With multiple channels in azimuth receiving echoes, multichannel radar has the potential of forward-looking imaging, and various schemes can be formulated. However, due to the different resources utilized by different imaging schemes, the angular resolution will be different. How to analyze the angular resolution and then design appropriate parameters is a key issue in multichannel radar forward-looking imaging. In this paper, based on the echo model of forward-looking imaging, the imaging schemes of synthetic aperture and real aperture are illustrated firstly. Then, based on the ambiguity function theory, the angular ambiguity functions, and the analytical expressions of the angular resolution for different imaging schemes are derived and analyzed. Finally, the simulation results of point targets and extended targets are presented to verify the effectiveness of the theoretical analysis, which would lay a significant foundation for the design of forward-looking imaging schemes of multichannel radar.

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.

Multichannel radar can resolve left/right ambiguity and has the potential of high-resolution forward-looking imaging. However, when only a limited number of channels are available, in order to have good performance of resolving left/right ambiguity, there is always grating lobe in the imaging results. In this paper, a scheme of grating lobe suppression based on self-supervised contrastive learning is proposed. Firstly, a generative adversarial network is employed to achieve the mapping from the source domain X (i.e. the imaging results with fewer channels) to the target domain Y (i.e. the imaging results with more channels). Then, a contrastive loss is introduced to ensure across-domain preservation of the content information like the distribution of ground objects, preventing the generator from making unnecessary changes in results when suppressing the grating lobe. At last, simulation results are given to illustrate the effectiveness of the proposed method.

Radar forward-looking imaging is important in many fields, such as the ground mapping, autonomous driving. However, conventional monostatic SAR cannot realize forward-looking imaging due to left/right ambiguity. In this paper, based on the geometry model of multichannel forward-looking SAR, the principle of resolving left/right ambiguity is illustrated firstly, and then the performance is analyzed. At last, simulation results are illustrated to verify the effectiveness of theoretical analysis.

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.

Radar forward-looking imaging has important applications in autonomous landing, autonomous navigation, reconnaissance guidance and other fields. However, conventional single channel synthetic aperture radar (SAR) or Doppler beam sharpening (DBS) technology has a blind area for forward-looking imaging due to left/right ambiguity and small angle variation. Multichannel radar can utilize the differences of echoes from multiple channels in azimuth to resolve left/right ambiguity, and has the potential for forward-looking imaging. However, there is still a problem of low azimuth resolution due to the restriction of array size. In this article, a deep learning based multichannel radar forward-looking super-resolution imaging framework is proposed. In this framework, synthetic aperture processing is conducted on the echo data of each channel to obtain the image with left/right ambiguity, and the preliminary forward-looking imaging is achieved first by resolving left/right ambiguity with multichannel data. Then, the generative adversarial network (GAN)-like network with mixed attention mechanism is designed to learn the mapping relationship between the original scene and the preliminary imaging result. At last, based on the learned mapping relationship, the echo data of multichannel radar is processed with the proposed framework to achieve forward-looking superresolution imaging. Experimental results were provided to verify the effectiveness of this imaging framework.

Sidelobe suppression is an important step in SAR imaging, especially for forward looking areas with poor azimuth resolution. In this paper, the signal model of multichannel forward-looking SAR imaging is realized with BP algorithm firstly, and then spatial smoothing coherence factor(SSCF) is introduced to suppress sidelobes of the imaging results. At last, simulation results are given to illustrate 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.

Conventional monostatic SAR or DBS technology cannot realize forward-looking imaging due to Doppler symmetry ambiguity. With multiple channels in azimuth, multichannel radar can resolve the left/right ambiguity, and have the potential for forward looking imaging. However, multichannel radar have flexible channel configuration, and there are different processing schemes, which may have different imaging performance and challenges. In this papar, based on the signal model of SIMO radar, different processing schemes for multichannel radar forward-looking imaging are illustrated and simulated, as well as the corresponding potential and challenges.

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.

Multichannel SAR can solve the problem of left/right ambiguity and has the potential of high-resolution forward-looking imaging. However, its azimuth superresolution in the adjacent area of flight path is still poor due to the small angle variation. In this paper, we propose a multichannel forward-looking SAR azimuth superresolution method based on CycleGAN. First, the multichannel forward-looking SAR imaging results are obtained, and then the mapping between forward-looking imaging results and the ground truth is learned with CycleGAN, to achieve azimuth superresolution. At last, simulation results are given to illustrate the effectiveness of the proposed method.

Imaging and target detection are usually regarded as two independent parts in conventional processing, which means that the detection performance will be affected by the imaging result. In this paper, the LRSD-ADMM-net is proposed to achieve simultaneous super-resolution imaging and target detection for forward-looking scanning radar. In addition, simulation results were provided to verify the effectiveness of the proposed algorithm.

Multichannel radar has the potential of forward-looking imaging with multiple channels receiving echoes on a single platform, but its azimuth resolution is usually poor. Superresolution algorithms have been developed to solve the problem, however, most of the methods have problems of difficulty in parameter adjustment and large amount of computation. In this paper, driven by the powerful learning ability of deep networks, the conventional ISTA reconstruction process is mapped into a deep network, and then the deep unfolding ISTA-Net is formed and used to realize multichannel radar forward-looking superresolution imaging. The simulation reults verify that the proposed method can provide high-quality reconstruction results while substantially reducing the imaging time.

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.

Conventional monostatic SAR or DBS technology cannot realize forward-looking imaging due to Doppler symmetry ambiguity. Although the ambiguity can be resolved by using multiple channels in azimuth, its azimuth resolution in the vicinity of flight path is usually poor due to the small angle variation. To solve the above problems, a hybrid real/synthetic aperture scheme for multichannel radar forward-looking imaging is proposed in this paper. In the scheme, the synthetic aperture imaging result with left/right ambiguity is obtained firstly using the information of platform motion. Then the real aperture superresolution imaging with regularized iterative adaptive approach(RIAA) is achieved using the instantaneous data of multiple channels. At last, the two imaging results are fused to obtain the forward-looking superresolution image without left/right ambiguity. Simulation results are given to illustrate the effectiveness of the proposed scheme.

Receiving signals sequentially through multiple channels in azimuth, 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 geometric distortion, a modified iterative adaptive algorithm(IAA) method is proposed to realize multichannel radar forward-looking superresolution imaging. Simulation results are illustrated to verify the effectiveness of the method.