Different from the traditional wide-swath synthetic aperture radar (SAR) system, ultra-wide swath SAR can achieve ultra-wide swath observation without the expense of an unimpaired azimuth resolution. However, due to the limitation of one receiving window, the radar echoes of near-range and far-range scatters arrive at the SAR receiver simultaneously, and this leads to the range ambiguity occurrence. This paper proposes a novel method based on chaotic frequency modulation (CFM) signals is proposed to suppress range ambiguity in ultra-wide swath synthetic aperture radar (SAR). Time-variant transmission avoids mutual interferences from the radar echoes of near-range and far-range scatters due to the uncorrelated property. Simulations and experiments on raw data are performed to verify the effectiveness of the proposed method.

Due to the sub-Nyquist sampling, compressive sensing (CS) theory can relieve the contradiction between high-resolution and wide-swath in the field of microwave imaging and it has attracted extensive attention. However, conventional CS-based imaging models always require sparse properties of the unrecovered scene. This paper proposes a synthetic aperture radar (SAR) change imaging in the transforming domain based on CS algorithms, which converts the recovery of the observed scene to that of scene change between the historical observation and the current observation. Firstly, in the sparse transforming domain constructed by historical observation, a new complex-data sparse microwave imaging model is built by the amplitude-phase separated operation. And then a block coordinate descent algorithm is used to recover the change with sub-Nyquist sampling echoes. At last, 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.

Staggered synthetic aperture radar (SAR) is an innovative concept of high-resolution and wide-swath systems, it combines SCan-On-REceive (SCORE) with continuous variation of the pulse repetition interval (PRI) to deal with the blind ranges over wide areas. Since the acquired data is not uniformly sampled and is partially lost, proper reconstruction methods should be considered to achieve good image quality. The existing reconstruction algorithms mostly resample the nonuniformly sampled signal into a uniform grid and then perform traditionally focused processing. However, these algorithms lack robustness with regard to the PRI strategies, especially under low oversampling or even sub-Nyquist sampling. In this article, a reconstruction algorithm based on Bayesian compressive sensing (BCS) is proposed for low-oversampled staggered (LS) SAR in the presence of an optimum random strategy. Simulations and experiments on raw data generated in LS SAR are performed to verify the effectiveness of the proposed method.

A novel compressive sensing (CS) synthetic aperture radar (SAR) called Sub-Nyquist SAR based on pseudo-random space-time modulation has been proposed to increase swath width while preserving the azimuthal resolution with a single azimuthal channel. For this SAR system, this paper presents an error correction method integrated with a ${\mathcal{L}_1}$-norm optimization algorithm to eliminate defocusing for the inaccuracy of the motion-induced model. Firstly, the exact mapping model is established. And then considering that CS algorithms themselves have a certain capacity of correcting phase error, we joint scene reconstruction based on ${\mathcal{L}_1}$-norm optimization algorithm and error correction to eliminate the defocusing caused by phase error. This proposed method regards the phase error as the model error and removes it during scene reconstruction. At last, experiments with strip-map TerraSAR-X images were carried out to demonstrate the remarkably improved performance of proposed algorithm.

A novel sub-Nyquist SAR based on pseudo-random space-time modulation has been proposed to increase swath width for the sparse scene while preserving the azimuthal resolution. Comparing to the traditional high-resolution wide-swath (HRWS) system, e.g., the azimuthal multi-channels SAR and multi-input multi-output (MIMO) SAR, with large antenna and amount of data, it applies single-channel and overcomes the limitation of Nyquist theorem based on compressive sensing (CS) theorem. CS algorithms are important to sub-Nyquist SAR imaging, and include three algorithms, i.e., greedy algorithm,$\mathcal{L}_{1}$-norm optimization algorithm and Bayesian-based method. This paper presents the comparative work of sub-Nyquist SAR imaging based on pseudo-random space-time modulation under different CS algorithms by simulation of real SAR images.

SAR based on compressed sensing (CS) greatly reduces the amount of data. The pseudo-random space-time modulation technology could alleviate the constraint on the type of the observed scene. This paper provides a survey on the effects of noise, sparsity, and phase on the modulation technology performance. Selection of a suitable algorithm is necessary to achieve this goal. l1-norm algorithm performs the best of the three algorithms, including greedy algorithm, and Bayesian algorithm. The experiment results show that the performance of the pseudo-random space-time modulation SAR is improved. With the increase of the noise and the decrease of the sparsity, the performance improvement with modulation is more and more limited. With the decrease of phase density, the performance obtained by modulation decreases continuously. When the amplitude variation range exceeds [0,π], the improvements are similar.

Because of the unprediction of the scene scattering characteristic and the finite quantization bits, saturated data always exists. Saturation phenomenon leads to a non-linear distortion and interferes to the recognition of the target so that it affects the image quality. Especially when the scene scattering characteristic largely varies, it can generate false targets and degrade signal-to-noise ratio (SNR). Compressed sensing (CS), a non-linear reconstructed algorithm, is that samples in sub-Nyquist rate is used to recover the sparse signal with few non-zero elements. This paper proposes the recovery method based on the nonlinear characteristic of CS to recover the saturated part of the raw data to the unsaturation state and ensure the unsaturated parts maintain the original state. Simulation results validate the proposed method.