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Abstract In this paper, the application of a hybrid representation in wide‐angle synthetic aperture radar (WASAR) imaging is investigated, addressing the challenge of achieving sparse representation in the presence of complex electromagnetic scattering characteristics and highly anisotropic targets. A convolutional neural network is utilized to represent the two‐dimensional data within each azimuth aperture, while employing blind compressed sensing (BCS) to achieve sparse representation across different azimuth apertures. Convolutional neural network (CNN) excels in learning spatial hierarchies and local dependencies of two‐dimensional data but requires a large amount of training data. Isotropic targets within each azimuth aperture can be used for training conventional SAR data, while acquiring training samples for anisotropic targets poses challenges. To address this issue, BCS are integrated into WASAR imaging, eliminating the need for additional training data. By integrating these methods, a novel approach called hybrid‐WASAR is proposed, which incorporates two regularization terms into WASAR imaging and employs the alternating direction method of multipliers to iteratively solve the imaging model. Compared to traditional WASAR imaging techniques, hybrid‐WASAR improves the accuracy of reconstructing the backscattering coefficients of the targets, leading to a significant enhancement in overall imaging quality.

Airborne forward-looking radar (AFLR) has been more and more impoatant due to its wide application in the military and civilian fields, such as automatic driving, sea surveillance, airport surveillance and guidance. Recently, sparse deconvolution technique has been paid much attention in AFLR. However, the azimuth resolution performance gradually decreases with the complexity of the imaging scene. In this paper, a data-driven airborne Bayesian forward-looking superresolution imaging algorithm based on generalized gaussian distribution (GGD- Bayesian) for complex imaging scene is proposed. The generalized gaussian distribution is utilized to describe the sparsity information of the imaging scene, which is quite essential to adaptively fit different imaging scenes. Moreover, the mathematical model for forward-looking imaging was established under the maximum a posteriori (MAP) criterion based on the Bayesian framework. To solve the above optimization problem, quasi-Newton algorithm is derived and used. The main contribution of the paper is the automatic selection for the sparsity parameter in the process of forward-looking imaging. The performance assessment with simulated data has demonstrated the effectiveness of our proposed GGD- Bayesian algorithm under complex scenarios.

Phased array radars have remarkable advantages over radars with single-element antenna in terms of agility, flexibility, robustness, and reconfigurability. Current pure-electronic phased array radars face challenges when operating with a large frequency tunable range and/or with broad instantaneous bandwidth. Microwave photonics, which allows wide bandwidth, flat frequency response, low transmission loss, and immunity to electromagnetic interference, is a promising solution to cope with issues faced by pure electronics. In this paper, we introduce a general architecture of microwave photonic array radar systems and review the recent advancement of optical beamforming networks. The key elements for modelling the response of the true time delay (TTD) and/or phase-shifting unit are presented and discussed. Two typical array antenna structures are introduced, i.e., microwave photonic phase shifter based array and optical true time delay based array, of which the principle and typical implementations are described. High-resolution inverse synthetic aperture radar (ISAR) imaging is also realized based on a microwave photonic array radar. The possibility of on-chip integration of the microwave photonic array radar is discussed.

Ground moving target imaging finds its main applications in both military and homeland security applications, with examples in operations of intelligence, surveillance and reconnaissance (ISR) as well as border surveillance. When such an operation is performed from the air looking down towards the ground, the clutter return may be comparable or even stronger than the target’s, making the latter hard to be detected and imaged. In order to solve this problem, multichannel radar systems are used that are able to remove the ground clutter and effectively detect and image moving targets. In this feature paper, the latest findings in the area of Ground Moving Target Imaging are revisited that see the joint application of Space-Time Adaptive Processing and Inverse Synthetic Aperture Radar Imaging. The theoretical aspects analysed in this paper are supported by practical evidence and followed by application-oriented discussions.