In microwave remote sensing applications, some areas need to be observed frequently. Compared with the historical observations, the current observation has a little new content. This means that the increment is sparse, making it possible for imaging with under sampled echoes. For the increment imaging, we first establish the linear observation model and the increment model, making it a two-variable optimization problem. Then, the block coordinate descent strategy is used to implement the increment imaging. To estimate the unknown phase matrix, we make the uniform quantification assumption, and an extended Lagrangian multiplier method is applied. For the sparse increment, Bayesian compressed sensing (BCS) is used. Two different resolution scenes are used to verify the feasibility of this algorithm. The performance under different signal-to-noise ratios (SNRs) and undersampling ratios has been discussed as well.

Geosynchronous synthetic aperture radar (GEO SAR) has the potential for conducting long-term observation of target zones, which is essential for remote sensing applications such as disaster monitoring and vegetation measurements. The squint imaging mode is crucial for long-term observation using GEO SAR. However, this type of SAR imaging is problematic because the squint mode introduces a nonzero range cell walk, which increases the prevalence of invalid data in echoes and intensifies the coupling between the azimuth and range. Therefore, this paper proposes a novel squint mode GEO SAR imaging method based on the correction of the bulk range walk of received signals. Adjusting the starting time of the receiving window significantly reduces the redundancy in echoes. Then, first-order filtering, range cell migration correction, range compression, partial dechirp, and azimuth compression are used to obtain the imaging result. Simulation results for the GEO SAR imaging of Wenchuan County in China demonstrate that the proposed algorithm can achieve a resolution of 5 m within a 30 × 30 km swath over 48% of the orbital period.

Ultra-wide swath synthetic aperture radar (SAR) systems are of great significance for applications such as terrain measurement and ocean monitoring. In conventional SAR systems, targets echo from the near-range and far-range of an observed swath mutually overlap, and the blind ranges are caused by those that the radar cannot receive while it is transmitting. Therefore, the swath of conventional SAR systems is limited due to their range ambiguity as well as the transmitted pulse blockage. With the development of waveform diversity, range ambiguity can be suppressed by radar waveform design with a low-range sidelobe without increasing the system’s complexity when compared to the scan-on-receive (SCORE) based on digital beamforming (DBF) technique. Additionally, by optimizing the pulse repetition interval (PRI) variation strategy, the negative impact of blind range on imaging can be reduced. Based on these technologies, this paper proposes a theoretical architecture to achieve an ultra-wide swath SAR imaging system via chaotic frequency modulation (FM) signals and a random pulse repetition interval variation strategy without increasing the antenna area. By transmitting time-variant chaotic-FM signals, the interference between targets in the near range and far range can be reduced by the corresponding match filters. Furthermore, random pulse repetition intervals increase the irregularity and aperiodicity of the blind ranges to further improve the imaging quality. Simulation results demonstrate that the proposed wide-swath SAR system has better performance compared to classical SAR systems.

A novel compressive sensing (CS) synthetic-aperture radar (SAR) called AgileSAR has been proposed to increase swath width for sparse scenes while preserving azimuthal resolution. AgileSAR overcomes the limitation of the Nyquist sampling theorem so that it has a small amount of data and low system complexity. However, traditional CS optimization-based algorithms suffer from manual tuning and pre-definition of optimization parameters, and they generally involve high time and computational complexity for AgileSAR imaging. To address these issues, a pseudo-${\mathcal{L}}_0$-norm fast iterative shrinkage algorithm network (pseudo-${\mathcal{L}}_0$-norm FISTA-net) is proposed for AgileSAR imaging via the deep unfolding network in this paper. Firstly, a pseudo-${\mathcal{L}}_0$-norm regularization model is built by taking an approximately fair penalization rule based on Bayesian estimation. Then, we unfold the operation process of FISTA into a data-driven deep network to solve the pseudo-${\mathcal{L}}_0$-norm regularization model. The network’s parameters are automatically learned, and the learned network significantly increases imaging speed, so that it can improve the accuracy and efficiency of AgileSAR imaging. In addition, the nonlinearly sparsifying transform can learn more target details than the traditional sparsifying transform. Finally, the simulated and data experiments demonstrate the superiority and efficiency of the pseudo-${\mathcal{L}}_0$-norm FISTA-net for AgileSAR imaging.

Since compressed sensing (CS) theory broke through the limitation of the traditional Nyquist sampling theory, it has attracted extensive attention in the field of microwave imaging. However, conventional CS-based imaging models always suffer from the limitation of sparse properties of the scene itself. In this article, a novel change imaging in the transforming domain based on CS is proposed, which converts the recovery of the scene itself to that of scene change from the historical observation to the current observation. Firstly, a new complex-data sparse microwave imaging model in the transforming domain is built by the real-imaginary separated operation. Then a scene transform method named inverse-whitening processing is introduced to confirm the relationship between the real part, imaginary part, and amplitude part of a complex scene, and the sparse transforming domain is constructed based on this processing and historical observation. At last, a CS algorithm is used to recover this change with undersampling echo, and the scene of the current observation can be achieved by integrating the recovered change with the historical observation. The effectiveness of change imaging in the transforming domain is verified on both simulated and real SAR images.

The nonuniform raw data due to the varying pulse repetition interval (PRI) and the loss of echo pulses inevitably introduce azimuth grating lobes in the low-oversampled staggered synthetic aperture radar (LS-SAR) images, which result in ghost artifacts. In this paper, a deconvolution-based grating lobes reduction method for LS-SAR images is proposed to improve image quality. Firstly, the position-invariant property of azimuth grating lobes is theoretically analyzed and verified, and the LS-SAR image on the same range cell is mathematically modeled as the convolution between the scattering scene and the point spread function (PSF) of the LS-SAR imaging system, accompanied by the additive noise. Then, the PSF is numerically calculated according to the LS-SAR sampling strategy, the measured azimuthal antenna pattern, and the BP (Back Projection) imaging method. Finally, based on the Lucy–Richardson (LR) iterative deconvolution principle, the recovery of observed scenes and grating lobes reduction can be simultaneously achieved by deconvoluting the LS-SAR image with the acquired PSF. Both simulated experiments with point-array targets and real SAR images, as well as validation experiments with airborne measured LS-SAR data, demonstrated the effectiveness of the proposed method.