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.

Sub-Nyquist synthetic aperture radar (SAR) based on pseudo-random time–space modulation has been proposed to increase the swath width while preserving the azimuthal resolution. Due to the sub-Nyquist sampling, the scene can be recovered by an optimization-based algorithm. However, these methods suffer from some issues, e.g., manually tuning difficulty and the pre-definition of optimization parameters, and a low signal–noise ratio (SNR) resistance. To address these issues, a reweighted optimization algorithm, named pseudo-ℒ₀-norm optimization algorithm, is proposed for the sub-Nyquist SAR system in this paper. A modified regularization model is first built by applying the scene prior information to nearly acquire the number of nonzero elements based on Bayesian estimation, and then this model is solved by the Cauchy–Newton method. Additionally, an error correction method combined with our proposed pseudo-ℒ₀-norm optimization algorithm is also present to eliminate defocusing in the motion-induced model. Finally, experiments with simulated signals and strip-map TerraSAR-X images are carried out to demonstrate the effectiveness and superiority of our proposed algorithm.