This paper describes the C-band multi-polarization Synthetic Aperture Radar (SAR) sensor for the Gaofen-3 (GF-3) mission. Based on the requirement analysis, the design of working modes and SAR payload are given. An accurate antenna model is introduced for the pattern optimization and SAR performance calculation. The paper concludes with an overview of predicted performance which was verified by in-orbit tests.

Synthetic aperture radar (SAR) systems are active imaging sensors that provide all-weather, day-and-night high-resolution radar images [1], [2], [3], [4], [5], [6], [7], [8], [9]. Moreover, SAR images can provide multidimensional information, including backscattering, polarization, and coherence information. With these unique advantages, SAR systems have been used in a wide range of applications, such as topographic mapping, geological exploration, vegetation characterization, forestry monitoring, disaster assessment, marine applications, and military reconnaissance.

Synthetic aperture radar (SAR) tomography (TomoSAR) has been well established for the three-dimensional (3D) reconstruction of urban buildings. Many methods have been proposed in the literature for TomoSAR inversion. These methods usually require fairly large data stacks (more than 20 images) for reliable reconstruction. Hence, they cannot be applied to the 3D reconstruction of urban buildings using Gaofen-3 (GF-3) data directly, because there are few images available on average in each city. This paper proposes a novel workflow for the 3D reconstruction of high-rise buildings using small data stacks. In this workflow, we combine the methods of contour line extraction (CLE) and reference-elevation multilooking relaxation (RM-RELAX) for the 3D reconstruction of high-rise buildings. The CLE method extracts contour lines of buildings relying on a data-driven approach and does not need to rely on any external data, which can provide prior knowledge for subsequent processing. The RM-RELAX method is an extension of RELAX, which can obtain more precise 3D inversion with multilooking and reject outliers with the reference elevation (which can be obtained by the prior knowledge of contour lines) of each pixel. The applicability of this workflow is demonstrated by using simulated data and real data with six SAR images acquired by the GF-3 satellite over an area in Beijing, China.

The Space Situational Awareness (SSA) program places great significance on obtaining high-resolution images of satellite in space orbit. By utilizing space-borne inverse synthetic aperture radar (SBISAR) can achieve high-resolution imaging of Observed satellite (OS) on-orbit especially in geosynchronous orbit (GEO). However, the complex and nonuniform relative motion of the OS on-orbit and SBISAR produces 2-D spatial variant phase errors, which has a high-order form during long coherent processing intervals (CPI). Up to now, the imaging problem of SBISAR has not been effectively tackled. In this work, a novel method to compensate for the 2-D spatial variant phase errors for SBISAR imaging based on minimum entropy and quasi-Newton's method is proposed. First, the geometric model that considers the relative motion state of SBISAR and the OS on-orbit is established and the optimal observation time period is determined. Second, we propose the echo signal model for the satellite on-orbit and deduce the specific form of the high-order spatial variant phase errors. Third, based on image entropy (IE), quasi-Newton's method is adopted to obtain the optimal solution for the phase error coefficients. Finally, a new initial estimation method that aims to overcome the local convergence of quasi-Newton's method is proposed. By utilizing the optimal parameters, the well-focused SBISAR image can be achieved. Taking GEO satellite imaging as an example, experiments based on scattering point simulation data verify the effectiveness of the proposed method.

In the bistatic synthetic aperture radar (BiSAR) system, the unavoidable frequency deviation between the oscillators (USOs) will result in additional phase modulation in the demodulated radar signal, which significantly degrades the quality of the SAR image and digital elevation model (DEM) product. The innovative L-band spaceborne BiSAR system LuTan-1 (LT-1) employs a non-interrupted synchronization scheme to acquire the synchronization phase error. This advanced phase synchronization scheme avoids interrupting the normal BiSAR data acquisition and further increases the synchronization frequency. However, some non-ideal factors in the transmission link like attenuation, multipath effect, interference, etc., may cause the synchronization phase to be polluted by noise. A phase denoising approach based on compressive sensing (CS) is proposed to improve the accuracy of synchronization phase. The imaging phase with high signal-to-noise ratio (SNR) is input into the K-SVD algorithm to learn the prior information, and then the noise of the synchronization compensation phase is eliminated by maximum a posteriori (MAP) estimation. The data acquired from the ground validation system of the LT-1 synchronization module are adopted for the validation experiment. The proposed phase denoising method achieves higher phase synchronization accuracy compared with traditional ones. The processing results verify the effectiveness of the proposed method and demonstrate its potential for future on-orbit applications of the LT-1 mission.