With regard to inverse synthetic aperture radar (ISAR) imaging, traditional range-Doppler (RD) algorithm is inapplicable to sparse aperture. Although compressive sensing (CS) algorithm can overcome this problem, the imaging resolution is not high enough. When deep learning (DL) is applied to ISAR imaging, some problems may also occur, such as network complexity, the selection of labels in network training, the influence of noise and sparse aperture, and the loss of weak scattering points in network test. In order to solve the above problems, a joint fast iterative shrinkage-thresholding algorithm (FISTA) and Visual Geometry Group Network (VGGNet) high-resolution imaging method is proposed in this paper. In the proposed method, FISTA is presented to reduce the impact of noise and sparse aperture. The high-resolution processing network (HRPN) is built based on VGGNet. Then, combined with peak extraction technology, the random ideal scattering points are utilized to construct the training/validation set. Meanwhile, the training process of HRPN is analyzed theoretically, and the network test strategy is designed by the differences between the test set and the training/validation set. Extensive experiments based on both simulated and measured data demonstrate that the proposed method has good imaging performance and small network complexity. INDEX TERMSInverse synthetic aperture radar (ISAR), Deep learning (DL), Compressive sensing (CS), High-resolution processing network (HRPN), Peak extraction technology.

Conventional inverse synthetic aperture radar (ISAR) imaging with sparse aperture usually suffers from high side lobes and wide main lobes, which limit the applications of radar super‐resolution imaging, multi‐target resolution, and cognitive reconfiguration. This paper proposes a fast, super‐resolution imaging method employing continuous compressive sensing for sparse‐aperture ISAR. First, the received echo in each range bin is characterised as a linear combination of multiple frequencies shown in a continuous atomic set, established into an atomic norm minimisation (ANM) mode. Second, to improve the resolution and reduce the computational burden significantly, a locally convergent iterative algorithm based on the alternating direction method of multipliers, which iteratively performs ANM with a sound reweighting strategy, is implemented. Then, the low‐rank Toeplitz covariance matrix, which contains the information of the target, is obtained. Subsequently, the Vandermonde decomposition of the Toeplitz covariance matrix is performed to acquire the locations and intensities of the scattering points. Finally, the super‐resolution result is generated by depicting the estimated scatterers in the image. Extensive numerical experiments demonstrate that the proposal is highly effective in recovering the super‐resolution image and shows better performance than state‐of‐the‐art methods.

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