Since the start of the Sentinel-1 (S-1) mission, numerous cases of severe image degradation caused by radio frequency interference (RFI) have been reported, which puts forward an urgent need for RFI identification and mitigation. In this article, an automatic RFI identification method is proposed based on a Siamese-type deep convolutional neural network (Siam-CNN-RIM). The Siam-CNN-RIM can be served as a preprocessing step before RFI mitigation to identify whether an S-1 image is RFI-contaminated or not. Different from traditional RFI identification networks which only use a single image as input, an additional image in the repeat-pass time series is also fed into the input of Siam-CNN-RIM as a reference. Both the input images correspond to the same illuminated area and pass through the same convolutional layer followed by an energy function such that the different features caused by RFI can be extracted and the background terrain features can be ignored. This is beneficial for distinguishing the real RFI signatures and the similar terrain signatures that may cause false positives and thus improving the RFI identification performance. Experimental results show that the proposed method is robust in different scenarios and can achieve more than 97% RFI identification accuracy, even for the open-set task where the test scenarios are not included in the training set.

In radar detection, small and slow targets are easily overwhelmed by strong clutter. Traditional methods, such as singular value decomposition (SVD) and robust principal component analysis (RPCA), can suppress clutter and recover targets by using low-rank and sparse models. However, these methods rely on fixed prior information, which lacks adaptivity and suffers from unfavorable extensive manual hyperparameter tuning. To address these issues, a data-driven deep network model combined with an iterative algorithm called unfolding joint sparse recovery network (UFJSR-Net) is proposed to achieve the improved target detection performance. First, a joint sparse recovery (JSR) model is established and the fast iterative shrinkage/thresholding algorithm (FISTA) is derived to solve this model. Then, one iteration consisting of a linear operation and a nonlinear one can be recast into a single network layer and the stacking and combination of all layers will form the UFSJR-Net. Finally, the properties of the target and clutter can be learned by paired inputs and outputs training data to optimize the hyperparameters in the iterative algorithm to obtain the JSR model. Experiments on simulation data and measured radar data demonstrate that the proposed method exhibits advantages in detection performance over traditional decomposition methods under strong clutter with different intensities.

Waveform design for integrated radar and jamming is generally based on the concept of shared waveform, which uses jamming signals without typical radar signal characteristics for detection. Existing waveforms have shown limited design flexibility, high levels of sidelobe in detection results, and overall ordinary performance. We propose an integrated radar and jamming waveform based on smart modulation and complementary coding. Unlike traditional integrated radar and jamming waveform based on smart modulation, the phase angle of the binary phase-coded sequence is adjustable in this smart modulation method, allowing for a controllable jamming effect, achieving true smart modulation. However, this smart modulation waveform also suffers from high sidelobes in detection. To address this issue, we take a complementary coding approach and design a smart modulation waveform with complementary characteristics. This waveform can synthesize a complete linear frequency modulation (LFM) signal by adding two pulses together, thereby reducing the sidelobes in the smart modulation waveform and enhancing its detection performance. Theoretical analysis indicates that the detection and jamming effects of this integrated waveform can be flexibly controlled by adjusting the phase angles of the coding sequences. Simulation analysis and experimental results confirm the significant advantages of this waveform.

Synthetic aperture radar (SAR) systems are highly sensitive to interference, making interference suppression a critical concern. While significant research has been conducted on SAR interference suppression using parametric, non-parametric, semi-parametric methods, and machine learning, these studies often neglect the impact of high-power interference, which can saturate SAR receivers due to their limited dynamic range. This saturation can lead to the model mismatch and performance degradation of many previously studied suppression methods. In this paper, we provide a detailed analysis of saturated interference in SAR systems. We develop time-domain saturation signal models for different types of interference, and analyze the frequency domain spurious characteristics of saturated interference. Based on this analysis, we propose novel interference suppression method adapted for saturated narrowband and wideband interference. We validate our methods using semi-actual data from Radarsat-1, demonstrating their effectiveness in suppressing both narrowband and wideband saturated interference. Besides, we also conducted analysis and verification using the real saturated interference data of Gaofen-3. The results confirm the proposed methods' capability to enhance SAR performance under saturation conditions.

The integrated detection and jamming system (IDJS) aims to pose a jamming threat to the enemy radar, and perform target detection in the meanwhile. To pose an effective jamming, the frequency spectrum of the transmit signal of IDJS has to overlap with the operating band of the enemy radar. As a consequence, the transmit signal of the enemy radar (TSER) would also cause interference to our IDJS, thus degrading the target detection performance of IDJS. Such interference, however, was generally ignored in current literature on IDJS, which may lead to ineffectiveness in practice. In this paper, we propose two methods for IDJS to separate the interference of the enemy radar from the echoes of IDJS and enhance the target detection performance. In the first method, a time-varying filtering (TVF) is established in the short time Fourier transform (STFT) domain to suppress TSER. The second method is an enhancement of the first method. The sparsity of illuminated area in time-frequency (TF) domain is utilized and an adaptive dictionary based sparse recovery algorithm is derived to recover echoes of IDJS loss resulting from TVF. Simulation results indicate that two proposed methods can both effectively separate the TSER and echoes of IDJS for detection, and the IDJS can achieve excellent interference effects on enemy radar at the same time.