ISAR High-Resolution Imaging Method With Joint FISTA and VGGNet

Abstract

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.

Keywords

• Inverse synthetic aperture radar (ISAR)
• deep learning (DL)
• compressive sensing (CS)
• high-resolution processing network (HRPN)
• peak extraction technology