Elevation multichannel synthetic aperture radar (SAR) utilizes digital beamforming (DBF) technology to achieve high-resolution and wide-swath (HRWS) imaging. However, electromagnetic jamming sometimes occurs in SAR images, resulting in the loss of scene information. A DBF-SAR system is weak in resisting jamming signals, and the barrage jamming signals within an imaging swath are not easy to remove using traditional suppression methods for DBF-SAR. Thus, an advanced barrage jamming suppression scheme based on elevation multichannel cancellation is proposed. First, the jamming signals in the echoes are removed using a well-designed channel cancellation filter to obtain the jamming-free signals. In accordance with the characteristics of jamming-free signals, the weighting coefficients are rededuced, and a modified DBF method is presented to achieve HRWS imaging and improve the DBF-SAR signal-to-noise ratio (SNR). In this way, the proposed scheme can effectively eliminate barrage jamming signals. In addition, some simulations and a 16-channel airborne DBF-SAR experiment were executed to demonstrate the proposed scheme.

Synthetic Aperture Radar (SAR) is easily affected by various radio frequency interference (RFI), which leads to the degradation of image quality. In multi-channel SAR, the position of the RFI source is a prerequisite for most spatial filtering methods to remove interference. For dual-channel SAR systems, the existing RFI source localization methods can only obtain the position of a single jammer, while in a complex electromagnetic environment, there may be multiple RFI sources. To address this problem, a multisource RFI localization method for dual-channel SAR is proposed in this letter. First, the time-frequency analysis is employed to establish the feature mapping of RFI and SAR signals in the time-frequency domain. Then, ridge path detection and recombination algorithms are proposed to separate and extract the received interference signals. Finally, the least L ¹ -norm models are established for each extracted interference signal based on the phase difference of the two channels, and the high precision azimuth position of the RFI sources can be obtained using the interior point method. Simulations indicate that the azimuth localization accuracy of the proposed method can be within 20 m when the Signal-to-Interference Ratio (SIR) is below 0dB. The spaceborne SAR data acquired by Chinese Gaofen-3 (GF-3) also verify the effectiveness of the proposed method.

Jamming for synthetic aperture radar plays a important role in electronic countermeasures. Currently, there are many jamming methods, but each method has its own issues. Barrage jamming has the drawbacks of requiring high power and being easy to detect, while deceptive jamming faces issues such as inadequate real-time performance and the need for multiple parameters estimated with high precision. An intra-pulse modulation jamming scheme with a novel jamming effect is proposed to ameliorate these issues. Firstly, in the sidelobe, jamming parameters and optimal jamming weights are calculated by estimated parameters combined with the backscattering coefficient of the region of interest (ROI). Secondly, based on the jamming parameters, multiple intra-pulse modulations are simultaneously applied to the received ROI echo. Then the modulated signals are transmitted with corresponding optimal weighting power and superimposed with the original echo, leading to the damage of the ROI imaging results. Finally, the utilization of relatively low-power Gaussian noise for suppression resulted in complete covering of the target in the ROI. This scheme improves the real-time performance of generating jamming signals and significantly reduces the power required for noise for suppression. It analyzed that even in the presence of forwarding power errors and inadequate real-time performance, there will still be a significant jamming effect. Results of simulations and measured data validate that the proposed scheme can effectively destroy image information.

This letter proposes a scheme for automatically and accurately extracting the parameters of weak radio frequency interference (RFI) present in the signal within the time-frequency domain. This scheme utilizes Short-Time Fourier Transform (STFT) to transform each frame of radar echo containing interference into the time-frequency domain. In the time-frequency domain, a joint low-rank sparse robust principal component analysis based on truncated nuclear norms (TJLS-RPCA) is employed to divide the time-frequency representation into two joint low-rank sparse matrices, from which the interference parameters are extracted. Subsequently, K-means clustering is applied to determine the number of RFIs, and Gaussian fitting is applied to eliminate high-error items. This scheme can extract parameters such as the central frequency, bandwidth, and duration of the interference. The proposed scheme improves the accuracy of parameter extraction and processing speed, demonstrating notable effectiveness in handling weak interference. The Monte Carlo experimental results demonstrate that the scheme provides highly accurate parameter estimation when the Signal-to-Interference Ratio (SIR) is less than 13dB, with errors below 0.5%. Finally, the feasibility of the proposed scheme is validated using Sentinel-1A data, and the extracted results are consistent with the simulation results.

In the above article [1] , there is a correction to the author list. The author list is as follows:

Active jamming in complex electromagnetic environments can severely degrade radar remote sensing imaging and target detection, especially when deceptive and suppressive jamming components coexist. Existing deep learning methods usually formulate jamming recognition as a closed set classification task, which provides limited information about component superposition, time-frequency localization, and physical jamming parameters. To address these limitations, this paper proposes a prior-guided lightweight dual-task network for structured composite active jamming cognition. The proposed framework extracts multi-domain handcrafted features and decision tree based coarse priors from the received signal, and fuses them with short-time Fourier transform (STFT) time-frequency images through a confidence-gated MobileViT_CA-based network. The recognition branch predicts the jamming family, fine-grained class, and composite attributes, while the segmentation branch estimates component masks for copy, convolution, and noise components. A FiLM-conditioned mask refinement module further improves mask continuity and boundary quality, enabling the extraction of physical parameters such as bandwidth, center frequency, coverage duration, delay, slice width, and repetition interval. Experiments on a 22-class active jamming dataset show that the proposed method, built on a 1.92 M-parameter MobileViT_CA backbone, achieves 93.78% overall classification accuracy, 96.49% jamming family accuracy, and 89.17% accuracy on the 12 composite classes. The FiLM-conditioned mask refinement module improves the test-set mIoU from 67.55% to 82.93%, and most representative physical parameters are estimated with relative errors below 15%.