The Gaofen-3 (GF-3) data processor was developed as a workstation-based GF-3 synthetic aperture radar (SAR) data processing system. The processor consists of two vital subsystems of the GF-3 ground segment, which are referred to as data ingesting subsystem (DIS) and product generation subsystem (PGS). The primary purpose of DIS is to record and catalogue GF-3 raw data with a transferring format, and PGS is to produce slant range or geocoded imagery from the signal data. This paper presents a brief introduction of the GF-3 data processor, including descriptions of the system architecture, the processing algorithms and its output format.

Sparse signal processing has been used in synthetic aperture radar (SAR) imaging due to the maturity of compressed sensing theory. As a typical sparse reconstruction method, $L_1$ regularization generally causes bias effects as well as ignoring region-based features. Our team has proposed to linearly combine the nonconvex penalty and the total variation (TV)-norm penalty as a compound regularizer in the imaging model, called nonconvex and TV regularization, which can not only reduce the bias caused by $L_1$ regularization but also enhance point-based and region-based features. In this paper, we use the variable splitting scheme and modify the alternating direction method of multipliers (ADMM), generating a novel algorithm to solve the above optimization problem. Moreover, we analyze the radiometric properties of sparse-signal-processing-based SAR imaging results and introduce three indexes suitable for sparse SAR imaging for quantitative evaluation. In experiments, we process the Gaofen-3 (GF-3) data utilizing the proposed method, and quantitatively evaluate the reconstructed SAR image quality. Experimental results and image quality analysis verify the effectiveness of the proposed method in improving the reconstruction accuracy and the radiometric resolution without sacrificing the spatial resolution.

The technique of azimuth multichannel synthetic aperture radar (SAR) system has become a potential solution to the irreconcilable conflict between high-resolution and wide-swath (HRWS) confronted with in a traditional SAR system. Unambiguous imaging, especially for a scene with moving targets, is one of the crucial research topics in the HRWS SAR system. This paper proposes a simultaneous imaging scheme of moving targets and stationary clutter for maritime scenarios. First, the moving target echoes are extracted from the stationary clutter. After that, two methods working in completely different principles are used to estimate the radial velocity of each moving target, and the estimated result is used for phase compensation. After that, the moving target echoes are added back to the stationary scene echo and sent to the reconstruction filter. Lastly, the reconstructed echo can be processed by the classical Chirp Scaling (CS) algorithm. Experiments are carried out using the Chinese GaoFen-3 dual-channel data. The estimated velocities of the moving targets are verified by automatic identification service (AIS) information, and the imaging results show that the false targets are effectively suppressed and the moving targets also return to their correct positions along the azimuth.

Azimuth multi-channel Synthetic Aperture Radar (SAR) system operated in burst mode makes high-resolution ultrawide-swath (HRUS) imaging become a reality. This kind of imaging mode has excellent application value for the maritime scenarios requiring wide-area monitoring. This paper suggests a moving target detection (MTD) method of marine scenes based on sparse recovery, which integrates detection, velocity estimation, and relocation. Firstly, the typical phenomenon of scene folding in the coarse-focused domain is introduced in detail. Given that the spatial distribution of moving vessels is highly sparse, the idea of sparse recovery is utilized to acquire the azimuth time characterizing the position of the moving target reasonably. Subsequently, the radial velocity and position information about the targets are obtained simultaneously. What makes the proposed method effective are two characteristics of the moving targets in ocean scenes, high signal-to-clutter ratio (SCR) and sparsity of the spatial distribution. Then, estimation performances under different SCR are analyzed by Monte Carlo experiments. And the actual SCR of the vessels in the ocean scene obtained by GaoFen-3 dual-receive channel mode is invoked as a reference value to verify the effectiveness. Besides, some simulation experiments demonstrate the capability to indicate marine moving targets.

The synthetic aperture radar (SAR) is an important means of ship surveillance, but the motion of the ship leads to azimuth position offset, false targets, and azimuth defocusing for the spaceborne high-resolution and wide-swath (HRWS) SAR system, causing the degradation of imaging quality. The automatic identification system (AIS) can provide real-time information of the ships, which is an important auxiliary method for ship surveillance. Up to now, the traditional fusion of SAR and AIS mainly has focused on location matching and auxiliary recognition, and the next generation of GaoFen-3 (GF-3NG) satellite is equipped with both a SAR sensor and an AIS sensor to obtain the SAR images and simultaneous AIS information of ships. Consequently, this paper proposes a novel scheme to improve the imaging quality of moving ships for GF-3NG using AIS information. In this paper, through introducing the virtual stationary target, the slant range derivation (SRD) algorithm is proposed to estimate the radial velocity (RV) and the radial acceleration (RA) between the ship and the SAR platform relative to the stationary scene. According to the calculated RV, the azimuth position offset can be estimated and the ship can be repositioned on the image. After that, the traditional method is conducted to suppress the false targets. Finally, the method of using the RA to refocus ship slices is proposed. Additionally, the experiment results based on real data illustrate the effectiveness of the proposed methods.

Due to the specific working mechanism of alternately transmitting and receiving signals between multiple adjacent swaths, scanning synthetic aperture radar (ScanSAR) will cause periodical wavelike modulation of the intensity image along azimuth direction, which is known as scalloping. Conventional descalloping methods are achieved by proper azimuth antenna pattern (AAP) correction and multi-looking techniques but are limited by the accuracy of Doppler centroid estimating and the thermal noise. Another type of method extracts and suppresses the scalloping texture on the image, but the scanning parameters of ScanSAR are insufficiently considered. The period of scalloping on the image is related to the period of switching between subswathes. While the harmonics can be calculated by the period of a periodic signal, an improved frequency filtering method combined with imaging parameters is proposed. The scalloping modulation model of ScanSAR combined with imaging parameters is constructed, and the harmonics of scalloping texture are accurately calculated and filtered. For the low signal to noise ratio (SNR) image, the antenna pattern is modified according to SNR to avoid scalloping caused by noise. For non-uniform scenes, scalloping suppression is achieved by using the scalloping features acquired by uniform scenes. To separate the non-uniform scenes from uniform scenes, we still use the characteristics of harmonics caused by scalloping. Our descalloping method achieves accurate suppression of scalloping without sea-land segmentation and ship mask and the residual scalloping is reduced from 1.0 to 0.3–0.5 dB. The residual scalloping and statistical characteristics of the image are analyzed to demonstrate the effectiveness of the proposed method.