The performance of a deep learning-based Synthetic aperture radar (SAR) automatic target recognition (ATR) model largely relies on the scale and quality of training samples. However, it is time-consuming and expensive to collect sufficient data in practice. Although generative adversarial network (GAN) provides a way for SAR target image generation, existing GAN-based methods cannot confirm what features the generator learns, thus they struggle in generating precise SAR target images. In this paper, we propose an angle transformation GAN (ATGAN) that can generate azimuth-controllable SAR target images while preserving the target details. The key idea of our ATGAN is to reframe the generation task from the perspective of image-to-image translation. To this end, ATGAN consists of two modules, a coarse-to-fine generator that aims to learn the angle transformation in the deep feature space and then apply it to manipulate the representation of an input SAR target image to generate a new one, while a spectral-normalized patch discriminator that tries to estimate the probability that an input SAR target image is real rather than fake using a patch-averaged strategy. Combining with spatial transformer and adversarial training paradigm, ATGAN can generate precise SAR target images for ATR. Extensive experiments verify the effectiveness of the proposed ATGAN, and our method outperforms the state-of-the-art method qualitatively and quantitatively.

When synthetic aperture radar (SAR) is conducting remote sensing or terrain mapping, its radar beam is inevitably occluded by the variations in the under-test topography. Although back-projection algorithm (BPA) can theoretically directly solve the imaging problems of topography variations that most current SAR imaging algorithms cannot handle, these BPAs only solve the phase focusing of SAR echo signal, and do not consider the mismatch of SAR imaging results caused by topography occlusion. To solve the mis-imaging issue of the occluded area generated by BPA under the case of topography variation, a topography-based BPA (Topo-BPA) is proposed in this paper. Firstly, a new beam occlusion judgment algorithm based on spherical wave assumption is proposed, and its core is depression angle interpolation and depression angle updating. Then, the proposed Topo-BPA embeds the proposed beam occlusion judgment algorithm before the classical BPA, which not only did not reduce the focus depth of BPA, but improved the imaging accuracy of classical BPA. Finally, numerical experiments have demonstrated the superiority of the Topo-BPA’s performance in comparison with classical BPA.

The unknown relative motions between synthetic aperture radar (SAR) and a ground moving target will lead to serious range cell migration (RCM) and Doppler frequency spread (DFS). The energy of the moving target will defocus, given the effect of the RCM and DFS. The moving target will easily produce Doppler ambiguity, due to the low pulse repetition frequency of radar, and the Doppler ambiguity complicates the corrections of the RCM and DFS. In order to address these issues, an efficient ground moving target focusing method for SAR based on scaled Fourier transform and scaled inverse Fourier transform is presented. Firstly, the operations based on the scaled Fourier transform and scaled inverse Fourier transforms are presented to focus the moving targets in consideration of Doppler ambiguity. Subsequently, in accordance with the detailed analysis of multiple target focusing, the spurious peak related to the cross term is removed. The proposed method can accurately eliminate the DFS and RCM, and the well-focused result of the moving target can be achieved under the complex Doppler ambiguity. Then, the blind speed sidelobe can be further avoided. The presented method has high computational efficiency without the step of parameter search. The simulated and measured SAR data are provided to demonstrate the effectiveness of the developed method.

The space-moving target (SMT) poses a great challenge for coherent integration detection because of the across range cell (ARC) and Doppler frequency broadening (DFB) induced by the relative motions between SMT and radar platform. Moreover, the intricate azimuth Doppler ambiguity complicates the coherent integration of SMT. To tackle these challenges, a proficient method for the coherent integration of SMT detection under complex azimuth Doppler ambiguity is introduced. Firstly, a modified second-order keystone transform operation based on the new deramp function with regulatory factor is presented to address the quadratic ARC and Doppler spectrum ambiguity. Secondly, the linear ARC is eliminated by the proposed novel processing procedure based on the improved range frequency reversal transform and modified 2-D scaled Fourier transform without searching of Doppler ambiguity number. Thirdly, the DFB is removed by the 1-D scaled Fourier transform with minimal computational overhead. After that, the well-focused result of SMT is obtained. The proposed method demonstrates computational efficiency, as its implementation involves only the fast Fourier transform and complex multiplication operations, while also eliminating the need to search for Doppler ambiguity numbers. The presented method exhibits robustness against complex azimuth Doppler ambiguity and can effectively address the blind speed sidelobe. The multi-target scenario is further investigated, and a strategy for identifying spurious peaks caused by cross-terms is proposed. Real data results are provided to validate the method’s performance in scenarios involving both single and multiple targets.