With the increasing number of electromagnetic devices, radio frequency interference (RFI) suppression has gradually become an essential problem in synthetic aperture radar (SAR) imaging. Faced with complex RFI environments such as time-varying and multitype mixing that may occur, traditional approaches often result in inadequate suppression and loss of valuable echoes. Moreover, the representation ability of manually extracted features is limited, struggling to maintain consistent performance in complex electromagnetic environments. To tackle these challenges, this article proposes a feature-enhanced low-rank and sparse decomposition network (FELS-Net), which separates RFI and useful echoes in the time-frequency domain (TFD). We introduce two learnable invertible nonlinear transforms to enhance the representation of RFI and SAR echoes, and unfold the RFI suppression scheme based on low-rank and sparse decomposition into a parameter-learnable network structure. The strong interpretability of model-driven architecture offers a potential stability guarantee for RFI suppression performance, while deep learning (DL) contributes to more effective feature characterization and more efficient and robust parameter schemes. Experimental results demonstrate that the proposed method outperforms comparative approaches in both time-frequency (TF) and image domains, exhibiting robust performance across diverse experimental conditions.

Compared with the traditional tomographic synthetic aperture radar (TomoSAR) system, multistatic TomoSAR can overcome the physical size limitation, realizing high-resolution 3-D imaging via single pass. However, due to the minimum safety distance limitation between flight platforms, the maximum unambiguous imaging range of multistatic SAR is relatively small, which is difficult to meet the mapping needs of urban high-rise. To address this problem, this paper proposes a multistatic TomoSAR ambiguity suppression method based on multiple subbands. This method can effectively achieve ambiguity suppression and 3-D reconstruction of the target without grating lobes. First, a multistatic TomoSAR imaging model is established. Second, we analyze the mechanism of multiple subbands ambiguity suppression. Finally, we utilize the adaptivity of the sparsity Bayesian recovery via iterative minimum (SBRIM) algorithm to the number of targets to realize multistatic TomoSAR 3-D imaging. The effectiveness of the proposed method is validated by simulations.

Electromagnetic jamming can seriously affect the quality of synthetic aperture radar (SAR) images and pose significant obstacles to image interpretation. The additional degrees of freedom brought by multistatic SAR considerably contribute to the accurate extraction of jamming information. In this paper, a jamming detection and jammer localization method for the interrupted sampling repeater jamming (ISRJ) is proposed. First, the jamming components in multistatic SAR images are detected by utilizing the time delay characteristics of ISRJ. Then, based on the Doppler frequency invariance property of jamming, a system of equations for multiple receiving configurations is solved for jammer localization. The effectiveness of the proposed method is demonstrated through simulation.

With the increasing widespread use of synthetic aperture radar(SAR) systems in various environments, jamming have become a serious problem that they face. In many situations, these jamming affect SAR systems’ ability to gather information. Many anti-jamming methods are based on the classification of jamming types. In order to provide information about jamming types, it is necessary to design a classification method which can classify multiple types of jamming. Considering the complexity of jamming features, in this paper, multi-domain jamming feature analysis and a residual network with convolutional block attention module (CBAM) are proposed to classify SAR jamming. To train this jamming classification network and validate its effectiveness, a database containing different jamming simulations is generated. The simulation results show that this method has reliable classification performance for various types of jamming.

High sidelobe of strong scattering points usually submerges weak targets nearby and affects the quality of SAR image. Therefore, SAR image usually requires sidelobe control. Common window functions have limited improvement on PSLR performance when the image resolution is required to be guaranteed. Combining Min-Max weighted ISL technique, this paper proposes an azimuth low sidelobe window function design method for SAR. Simulation results show that PSLR of the designed window is nearly −10dB lower than hanning window with a −45dB ISL level, and main lobe width is almost equal to hanning window.

The survivability and stability of synthetic aperture radar (SAR) in the increasingly severe electromagnetic environment are widely concerned. With the development of interception receiver technology, it is very difficult to prevent the competitor from detecting the radio frequency (RF) energy of radar. Therefore, more complex intra pulse modulation waveform is needed to prevent the competitor from effectively acquiring and analyzing information. In this paper, a novel low probability of intercept (LPI) waveform optimization framework is proposed. Symmetric piecewise linear functions (PWL) is used to define the waveform search space and a constrained multi-objective evolutionary algorithm is employed to solve the waveform optimization problem. Simulation results show that the proposed waveform has good performance of low interception and auto-correlation, which is suitable for SAR imaging.

The clutter background poses a challenge to the detection and recognition of targets from synthetic aperture radar (SAR) images, especially when the target is submerged by the clutter. In this paper, we propose a target-oriented SAR waveform optimization method to deal with this problem. The proposed method constructs an optimization criterion jointing the signal-to-clutter ratio (SCR) and the resolution of transmitted waveform. Based on the prior information of the frequency response of the interested target, the clutter suppression performance and the range resolution performance are jointly optimized. The simulation results show that the proposed method can effectively improve the SCR of SAR image in the condition of low SCR, while the resolution performance is guaranteed.