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P- and L-band synthetic aperture radar (SAR) sensors have foliage- and ground-penetrating imaging capabilities and, thus, contribute to missions, such as deforestation monitoring, biomass estimation, and deformation measurements. The spectrums they employed are also allocated to many services, such as radio navigation satellite services, radio location services, telecommunication, and broadcasting, which is prone to generate harmful radio frequency interference (RFI) to SAR including time stationary narrow band (TSNB) and intermittently transmitted wideband (ITWB) versions. Researchers generally perform on-ground processing to mitigate RFIs by which notch filtering is the best known. However, the phenomena of side-lobe uplift and SNR degradation caused by spectral gaps will deteriorate SAR image quality and further affect image interpretation. This article models and characterizes the signature of TSNB and ITWB RFI in P- and L-band SAR, and on this basis, a two-stage approach is developed to mitigate RFI that overcomes the drawbacks of notch filtering-based approaches. In the first stage, a spectrum cancellation-based RFI detector is proposed, which simultaneously locates RFI and produces an orthogonal projection operator. In the second stage, a low-rank approximation model regulated by truncated nuclear norm, which is solved by the alternating direction method of multipliers, is established to recover the missing spectral information. Experiments are conducted extensively on simulated data as well as real-world data collected by advanced land observing satellite phased-array L-band synthetic aperture radar system and a P-band airborne SAR system to validate the proposed approach.

The electromagnetic environment is becoming complex as the usable spectrum will be allocated for more services. As a result of this situation synthetic aperture radar (SAR) missions are frequently perturbed by radio frequency interference (RFI) that jeopardizes their scientific observations all over the world. The state-of-the-art multichannel SAR has anti-RFI capability since it’s capable of digitally modulating the antenna pattern (AP) in postprocessing, thereby steering the null toward the angle of arrival (AOA) of the RFI in the spatial domain. However, the AOA of RFI is space-variant, meaning that the mitigation performance of beamformers sensitive to AOA will greatly deteriorate. In addition, the AOA of in-swath RFI and the target echo arrive simultaneously, thus the traditional beamformer will generate a distorted AP, deteriorating the SAR imagery. In light of these considerations, this article studies the in-swath and out-of-swath RFIs in elevation multichannel SAR and develops their countermeasures. Thereinto, a least $\ell _{1}$ -norm model is developed to estimate the AOA of the RFI, followed by two schemes developed to separate the RFI. The former develops a beamformer that joint sidelobe control and null expanding to mitigate the space-variant out-of-swaths RFI, whereas the latter develops a blind source separation (BSS)-based technology to mitigate in-swath RFI, avoiding AP distortion and restoring SAR imagery. The effectiveness of the proposed approaches is supported by experiments based on the measured X-band airborne DBF-SAR data as well as the simulated SAR data.

For azimuth multichannel (AMC) high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) systems, nonuniformly sampled signals will lead to false targets appearing in the image, which has a great impact on image quality and target detection. Many spectrum reconstruction methods for stationary scenes have been proposed to obtain images without false targets, but it is ineffective for moving targets due to the phase error caused by radial velocity. To present the radial velocity effect on the reconstructed image clearly, we establish a precise relation between the characteristics (imaging position, residual RCM, and amplitude) of false targets and radial velocity from the Doppler domain perspective. According to the effect analysis, we propose an image-domain false target suppression and radial velocity estimation method for moving targets. First, obtain multiple images through imaging preprocessing. Second, estimate and compensate the phase error of false targets based on the least $L^{1}$ -norm optimization model to suppress false targets. Third, estimate the radial velocity of real moving targets based on the cross correlation method. Compared to the existing methods, the proposed method is processed in the image domain which has a high signal-to-noise ratio (SNR) with the advantages of not requiring recognition and extraction of targets, lower computational complexity, and applicability for slow targets, fast targets, and multiple targets of the same range cell. The simulated SAR data and GaoFen-3 SAR data are processed to demonstrate the effectiveness of the proposed method. Furthermore, the radial velocity estimation method is verified by automatic identification system (AIS) information.

An important challenge in high-resolution inverse synthetic aperture radar (ISAR) imaging of maneuvering targets is the 2-D spatial-variant (SV) high-order phase error. The classic autofocusing phase correction method cannot effectively correct the SV phase error, which makes it difficult for the traditional range-Doppler (RD) algorithm to obtain high-quality images in high-resolution imaging. The range SV phase error can be compensated for each range cell separately after migration correction of the cross-range cell, but correcting the azimuth SV phase error is challenging. This article presents a high-resolution ISAR imaging algorithm for maneuvering targets with azimuth adaptive partitioning and phase compensation function estimation. First, the coarse-focus image is obtained by the azimuth Fourier transform (FT) after translational motion compensation and migration through range cell (MTRC) correction. Then, the azimuth coarse-focusing result is adaptively partitioned at each range bin. Each partitioned data block is transformed into the time domain, and joint time-frequency analysis (JTFA) is performed to obtain the time-frequency (TF) curve of the signal. Through alignment of TF curves, the phase compensation function is inverted. Finally, the corrected subblocks are stitched together to obtain high-quality ISAR images. ISAR imaging experiments with simulated and real data demonstrate the effectiveness and practicability of the proposed algorithm in high-resolution ISAR imaging of maneuvering targets.