Real-aperture radar can be utilized to realize forward-looking imaging by antenna scanning the imaging region. However, low azimuth resolution seriously affects its practical application. Although traditional super-resolution methods could enhance azimuth resolution to a certain extent, effective preservation of contour information for important targets still remains to be a problem. In this article, a method of total variation-sparse (TV-sparse) multiconstraint deconvolution is proposed to improve azimuth resolution of forward-looking imaging as well as preserve contour information of important targets. Since our interested targets usually appear to be sparse, the sparse constraint of the target is introduced first to achieve high resolution of forward-looking images, which may cause the loss of target contour information in the meantime. Second, total variation (TV) constraint is introduced based on the sparse constraint, converting traditional single-constraint super-resolution problem to a multiconstraint problem. We then use the split Bregman algorithm (SBA) to solve the multiconstraint problem, whose solution is the super-resolution image of radar forward-looking region. Compared with traditional super-resolution methods, the proposed method can improve the azimuth resolution of radar forward-looking imaging as well as better restore target contour information by adjusting respective weights of sparse constraint and TV constraint. Finally, the performance of the proposed method is validated with the simulation and measured data.

Forward-looking imaging and target detection are highly desirable in many military and civilian fields, such as search and rescue, sea surface surveillance, airport surveillance, and guidance. However, there is a blind zone of forward-looking imaging for conventional Doppler beam sharpening and synthetic aperture radar. Scanning radar can be utilized to obtain a real beam image of a forward-looking area and implement target detection, while its azimuth resolution is poor due to the limitation of antenna size. Besides, during the processing procedure, imaging and target detection are usually regarded as two independent parts, which means that the imaging result will directly affect the detection performance. In this article, an integrated algorithm of super-resolution imaging and target detection for forward-looking scanning radar is proposed. In this algorithm, first of all, low-rank and sparse constraints as regularization norms are incorporated into the forward-looking scanning radar imaging and the objective function is established. Subsequently, the convex theory is utilized to solve the objective function and transform the problem of simultaneous super-resolution imaging and target detection into an optimization problem. Lastly, the super-resolution imaging and the target detection results are obtained simultaneously by solving the optimization problem using the alternating direction method of multipliers. In addition, simulation and experiment results are given to verify the effectiveness of the proposed algorithm.

In order to obtain high resolution images of the objects in front of platform, a super-resolution scheme for multi-channel 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 correlation of pulse-compressed data of different channels is presented firstly, and then a revised steering matrix considering failure channels and motion error is constructed. Finally, the echo data is 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.

Scanning radar can be used to obtain images of targets in forward-looking area, and has attracted much attention in many fields, such as ocean monitoring, air-to-ground attack, navigation and so on. However, its azimuth resolution is extremely poor due to the limitation of the antenna size. In order to break through the limitation, many super-resolution algorithms have been proposed, and iterative shrinkage-thresholding algorithm is one of the most famous methods because of its anti-noise ability and simplicity. In the meantime, the slow convergence of iterative shrinkage-thresholding algorithm is also known to all. In this paper, a second-order accelerated iterative shrinkage-thresholding algorithm for scanning radar forwardlooking super-resolution imaging is proposed. In this algorithm, a prediction vector is constructed before each iteration by using the first and the second-order difference information of iteration vectors, to reduce the number of iterations and get a faster convergence speed. In the end, simulations and experimental results are given to illustrate the effectiveness of the accelerated imaging algorithm.

Radar forward-looking imaging has important applications in autonomous landing, autonomous navigation, reconnaissance guidance and other fields. However, conventional single channel SAR or 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 paper, 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 firstly by resolving left/right ambiguity with multichannel data. Then, the 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.

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.