Radio frequency interference (RFI) is an important factor affecting microwave remote sensing observations, causing random degradation of synthetic aperture radar (SAR) images. Due to the huge amount of raw echo and single-look complex (SLC) data, there are some SAR interpretation scenarios when only amplitude images can be obtained, and traditional signal transformation and matrix operations can hardly meet the suppression requirements in the absence of phase information at this time. Although deep learning algorithms have made some progresses on this issue, they still suffer from the following limitations: (i) There are few models dedicated to SAR RFI in the image domain; (ii) The network structure is relatively complex due to not fully exploit the physical characteristics of SAR and RFI. To this end, we propose an end-to-end suppression network (PMNet), which includes a novel explainable feature decomposition module (FDM) based on the idea of non-negative matrix factorization and a composite loss function to achieve dynamic separation of foreground and background features of supervised RFI-contaminated images. The ablation experiment proves that compared with the baseline algorithm, the visual similarity of the proposed PMNet on the test set can be improved by up to 14.15%. The suppression result on Sentinel-1 and Gaofen-3 real data also verifies the effectiveness of the PMNet in different SAR platforms.

The synthetic aperture radar (SAR) can be affected by various types of jamming during operation. Among them, the deceptive jamming generated by digital radio frequency memory (DRFM) jammers poses a serious threat to SAR imaging by creating highly realistic false targets. Moreover, with advancements in deceptive jamming technology, the generation speed of deceptive jamming has increased, rendering existing methods less effective. To address this issue, an anti-deceptive jamming method based on pulse repetition interval (PRI) variation design and multi-channel principle is proposed to mitigate the effects of deceptive jamming. First, a PRI variation strategy that will not cause the loss of echo signals in the imaging area is designed. By utilizing this strategy for imaging, deceptive jamming signals are dispersed across different ranges, resulting in preliminary suppression of the jamming. Subsequently, after azimuth non-uniform sampling reconstruction and range processing, most of the jamming signals are suppressed due to the azimuth timing differences between SAR and jamming signals. However, when the jammer uses specific retransmission intervals, such as the average PRI of the PRI sequence, the jamming signals may be concentrated at certain ranges, retaining some coherence and posing a threat to SAR imaging. To overcome this challenge, a residual jamming detection and suppression algorithm based on multi-channel principle is proposed, which can detect and filter out the channels affected by jamming. Finally, an azimuth sparse reconstruction is introduced for azimuth processing. Since the anti-jamming principle of this method relies on the differences in azimuth timing between SAR and jamming, it can suppress deceptive jamming even when the generation speed of deceptive jamming is rapid, which some other anti-deceptive jamming methods cannot achieve. Simulations of SAR imaging under deceptive jamming conditions are conducted for point target scene and complex targets scene. The simulation results show that the proposed anti-deceptive jamming method can effectively suppress deceptive jamming and enable high-quality imaging.

With the expansion of the surveillance area and resolution of spaceborne synthetic aperture radar (SAR) systems, the increasing amount of echo data requires further research on efficient imaging methods. The pulse repetition frequency (PRF) of traditional SAR needs to satisfy the Shannon–Nyquist sampling theory, while the PRF limits the system swath width, making it impossible to achieve wider illumination coverage. The reduction of the PRF can effectively increase the swath width, but it will cause severe azimuth ambiguity, decreasing the image quality. Several methods have been introduced for ambiguity suppression, but many of them become ineffective at lower PRFs. In this article, we propose a novel sparse imaging method for the spaceborne PRF-reduced sliding spotlight SAR. The proposed method considers the azimuth ambiguity term in the joint sparse imaging model, separately constrains the main imaging and azimuth ambiguity areas, and achieves azimuth ambiguity suppression by using compressive sensing (CS) technology. With its help, we can reduce the original PRF by up to half to double the swath width and enable high-precision sparse reconstruction of large-scale scenes. Compared with$L_{2,1}$-norm regularization-based algorithm, the proposed method shows the superior ambiguity suppression ability with less computational cost from PRF-reduced echo data. Experimental results on simulated raw data validate the proposed method.

An innovative approach, mainly featured by progressive iterations of partitioning and focusing, to processing very-high-resolution spaceborne synthetic aperture radar (SAR) data is presented in this article. Due to long integration time endured during data acquisition as well as large scene extension being common to the advanced SAR systems, spatial dependence of range histories may be severe, especially is the terrain undulation severe, and must be properly dealt with. In this article, after deriving the exact two dimensional spectrum for an curved satellite orbit, we focus the entire echo data through several iterations of partitioning and focusing. Besides, a new technique aiming at enhance the focusing quality of the final data blocks is also presented. Finally, the well-focused final image blocks are recombined into the entire image. Owing to the feature of partitioning, the spatial dependence of many factors can be easily accommodated. The methodology is validated by processing results on simulated and real SAR data.

Radio frequency electromagnetic interference is a relatively common phenomenon, especially for synthetic aperture radar (SAR) systems working in P- or L-band. Compared with the suppression of narrowband interference, that of wideband interference, particularly of those whose signal parameters have frequently changing property, is still a sophisticated problem. In this letter, a suppression algorithm for interference with wideband and complicated parameters is proposed, based on low-rank and sparse matrix decomposition (LRSMD) in time–frequency domain (TFD) of the signal. The proposed algorithm begins with transforming the SAR signal into TFD. After that, LRSMD based on bilateral random projection (BRP) is applied to decompose the time–frequency spectrum matrix into three parts, a low-rank matrix standing for interference, a sparse matrix standing for SAR signal, and a noise matrix. Finally, inversely transform the sparse matrix into the time domain to obtain SAR signal without interference. The proposed algorithm is applied to a single look complex (SLC) SAR data of Sentinel-1 to validate its effect and efficiency.

In the complex electromagnetic environment, radio frequency interference (RFI) from other radiation sources often conflicts with synthetic aperture radar (SAR) systems, which overlaps and destroys the useful data in the same frequency band, causing adverse impact to the quality of SAR imaging. When faced with wideband or mixed complicated RFI, traditional methods inevitablely damage the original signal, and cannot effectively protect and reconstruct the useful information. Besides, the current semi-parametric algorithms have large computations and limited generalization ability. To address these issues, this paper proposes a prior-induced-learning framework (PISNet) to achieve RFI suppression and useful signal recovery in time-frequency domain. Both narrowband and wideband interference are uniformly modeled as a sparse distribution in time-frequency domain, and the stationarity of SAR echoes determines its low-rank characteristic. These properties of RFI and SAR data are treated as prior knowledge to inject into our PISNet. An iterative reconstruction module is raised to achieve low-rank reorganization of the fused residual features. Meanwhile, a novel loss function is put forward to induce the network training to ensure that each component conform to the prior. The proposed approach innovatively integrates deep learning with semi-parametric methods for RFI suppression, which achieves superior performance on simulated and real data. Compared to existing learning-based methods, the image quality of the restored Sentinel-1 data is improved by 9.37% AG. The code and dataset will be available online (https://github.com/JyuanShen/PISNet).

Spotlight synthetic aperture radar (SAR) achieves very high-resolution (VHR) images by steering the azimuth beam during the formation of the synthetic aperture. In practice, the steering is implemented through discrete azimuth beam switching. Then, phase shifts can occur between the adjacent beams due to the error of the antenna pattern. In addition, the troposphere introduces a beam-angle-dependent delay to the echo. Those undesired phase shifts and delays cause phase errors in the received echo and result in image quality deterioration. In this letter, an algorithm, called the Newton entropy minimization (N-EM), is proposed to estimate and compensate the phase errors caused by the discrete azimuth beam steering for the spotlight SAR data. Combining with the subaperture imaging approach of the spotlight SAR, the algorithm estimates the phase offsets between each couple of the adjacent beams based on the minimum entropy criterion. The analytic expression, which is a nonlinear equation, is developed for the optimal estimation. Then, the Newton's method is employed to solve the equation. The real spaceborne SAR (both the staring and sliding spotlight modes) data processing results demonstrate the efficiency and accuracy of the proposed algorithm.

The inverse synthetic aperture ladar (ISAL) is an important method for observation and imaging of space targets. Here, a 3-D ISAL imaging algorithm is proposed for spinning targets such as space debris. Since laser wavelength is 4-5 orders of magnitude smaller than that of microwave, the Doppler frequency caused by target motion is more pronounced in ISAL. Doppler frequency modulation rates can be estimated by the fractional Fourier transform with respect to azimuth slow time even when the rotation angle is small such that scattering centers do not migrate through a range cell. Then, slant range, Doppler frequency, and Doppler frequency modulation rates form a 3-D space. The angular velocity and the incident angle can be estimated by the position relationship between the scattering centers in two observations. After image scaling, the 3-D shape and size of the target can be obtained. The 3-D structure of the target in the simulation experiment is accurately reconstructed. Monte Carlo experiments are conducted to discuss the effect of the signal-to-noise ratio and observation time on the algorithm. Finally, the effectiveness and robustness of the algorithm are verified.