Azimuth ambiguity may introduce false targets into synthetic aperture radar images, particularly likely in inshore and oceanic observation. To suppress the azimuth ambiguities for any acquisition mode, a new model is developed to describe the impact of spatially variant azimuth antenna pattern weighting on azimuth ambiguities. By accurately estimating the ratio of ambiguous to main zone energy based on the model, the proposed algorithm selects the subspectra with less ambiguous disturbance, and adopts extrapolation with weighted energy measure to obtain a full spectrum. Due to spectral selection and extrapolation, the novel algorithm achieves superior performance in azimuth ambiguity suppression and resolution preservation, which is compared with the classical algorithm, and validated by applying TerraSAR-X and RADARSAT-2 images.

Sidelobe suppression is a basic but important task for synthetic aperture radar (SAR) images, since the existing sidelobes can reduce the image quality and complicate image interpretation. The main task of sidelobe suppression is to suppress the sidelobes effectively while maintaining high image resolution and the mainlobe's energy. However, the most effective method, robust spatially variant apodization (RSVA), still faces the problem of energy loss on cluttered targets caused by phase factors. This letter proposes an SAR sidelobe suppression method based on the convolutional neural network (CNN) to compensate for the energy loss in RSVA. The experiments on the SAR images show better performance on both sidelobe suppression evaluation metrics and signal-to-noise ratio (SNR) preservation compared with conventional methods.

Geosynchronous synthetic aperture radar (GEO SAR) system has the ability of observing targets for several hours in a large field of view. Complex and irregular rotating motion of the ship on sea in long aperture time makes it difficult to focus. To accomplish the imaging of maneuvering ships, a novel imaging algorithm is proposed in this paper. A spatial filter constructed by convolutional neural networks (CNN) is utilized to extract the time-frequency features. Combined with local generalized radon-Fourier transform (GRFT) and phase gradient autofocus (PGA), the extraction accuracy further increases. Validation results based on simulation experiment, airborne experiment and wave pool experiment show that the proposed algorithm achieves good imaging performance.

Speckle noise can reduce the image quality of synthetic aperture radar (SAR) and complicate image interpretation. This study proposes a novel three-step approach based on the conventional probabilistic patch-based (PPB) algorithm to minimize the impact of bright structures on speckle suppression. The first step improves the calculation accuracy of the weight by pre-processing speckle noise with a linear minimum mean-square error filter and reassessing similarity between pixels. In the second step, an iterative method is developed to avoid interfering with bright structures and acquires a more accurate homogeneous factor by adaptively changing the size of the search window. In the final step, the spreading and blurring of bright structures is corrected using a modified bias-reduction technique. Experimental results demonstrate the proposed algorithm has improved performance for both speckle suppression and preservation of edges and textures, evaluated by indicators including the equivalent number of looks, the edge preservation index, the mean, and standard deviation of ratio images.

This paper develops a continuous tangent motion model by approximating the relative trajectories between spaceborne Synthetic Aperture Radar (SAR) and its targets during every transmitting period and every receiving period as tangential segments. Compared with existing motion models, including the stop-go model and the continuous rectilinear model, the continuous tangent model is much closer to the actual relative motion. Based on the new motion model, an imaging compensation algorithm is proposed that considers the space-variant property of SAR echoes more fully by introducing three space-variant factors: the time-scaling factor, the transmitting–receiving rate, and the transmitting–receiving constant. As a result, spaceborne SAR imaging with 0.21-meter resolution and wide-swath, up to 15 km × 15 km, is achieved. Simulation results indicate that the novel algorithm provides a superior and more consistent focusing quality across the whole swath compared with existing algorithms based on the stop-go or continuous rectilinear models.

Bistatic geosynchronous synthetic aperture radar (GEO SAR) system, with a geosynchronous illuminator and a stationary ground receiver, provides capability and flexibility of continuous observation and frequent revisit over certain area, which is of great significance for applications such as disaster monitoring. The Doppler frequency modulation (FM) rate in this configuration, however, varies along the azimuth direction, which increases difficulty in imaging. To equalize the Doppler FM rate, a new imaging method is proposed in this paper. By conducting time-Doppler resampling, the proposed method can remove the quadratic variance of Doppler FM rate at orbital positions where the Doppler FM rate is around zero. Validation results show that the proposed algorithm provides a better focusing quality compared with existing methods.