The next generation of synthetic aperture radar (SAR) systems will need transmit and receive analog signals over a wide frequency range with large bandwidth to fulfil the increasing demands. Since the performance of the digital microwave component is deteriorated at increasing frequency, radio frequency (RF) front-end with the capability of frequency conversion is needed for frequency beyond a few gigahertz. However, for the widely employed RF mixers, the inherent imperfect of I-V characteristics of nonlinear devices, generates many undesired mixing spurs which restricts on the instantaneous bandwidth and the spurious-free dynamic range (SFDR). At Aerospace Information Research Institute Chinese Academy of Sciences, a photonic-assisted front-end with functions of signal generation and deramp processing has been conceived and implemented in an experimental deramp-on-receive CW SAR system. The system is called PFDIR (Photonic-Assisted Front-End Deramp-on-Receive Imaging Radar) and is envisaged to obtain SAR imaging at high resolution. In current stage, the Ku-band system with a bandwidth of 5.72 GHz, corresponding to a relative bandwidth of 37.8%, operates well with a fixed two-horn antenna. The configuration of the photonic front-end and the system design are described. Ground-based tests and airborne imaging results with geometrical resolution down to 5 cm are presented.

We investigate the problem of obtaining dense 3D reconstruction from airborne multi-aspect synthetic aperture radar (SAR) image sequences. Dense 3D reconstructions of multi-view SAR images are vulnerable to anisotropic scatters. To address this issue, we propose a probabilistic 3D reconstruction method based on jointly estimating the pixel’s height and degree of anisotropy. Specifically, we propose a mixture distribution model for the stereo-matching results, where the degree of anisotropy is modeled as an underlying error source. Then, a Bayesian filtering method is proposed for dense 3D point cloud generation. For real-time applications, redundancy in multi-aspect observations is further exploited in a probabilistic manner to accelerate the stereo-reconstruction process. To verify the effectiveness and reliability of the proposed method, 3D point cloud generation is tested on $\mathrm{Ku}$-band drone SAR data for a domestic airport area.

Circular synthetic aperture radar (CSAR) has the potential to form 3D images with single-pass single-channel radar data, which is very time-efficient. This article proposes a volumetric neural renderer that utilizes CSAR 2D amplitude images to reconstruct the 3D power distribution of the imaged scene. The innovations are two-fold: Firstly, we propose a new SAR amplitude image formation model that establishes a linear mapping relationship between multi-look amplitude-squared SAR images and a real-valued 4D (spatial location (x, y, z) and azimuth angle $\theta$) radar scattered field. Secondly, incorporating the proposed image formation model and SAR imaging geometry, we extend the neural radiance field (NeRF) methods to reconstruct the 4D radar scattered field using a set of 2D multi-aspect SAR images. Using real-world drone SAR data, we demonstrate our method for (1) creating realistic SAR imagery from arbitrary new viewpoints and (2) reconstructing high-precision 3D structures of the imaged scene.

In this article, we present a novel synthetic aperture radar (SAR) 3-D imaging strategy using circular Interferometric SAR (InSAR) data. Our approach improves upon the Holographic SAR tomography (HoloSAR) techniques by eliminating the need for multi-baseline data collection nor residual motion error correction over a long curvilinear aperture. This may provide a simple yet effective 3-D imaging solution for perturbed airborne radar platforms. The key innovation is the first utilization of multi-aspect SAR interferograms to invert the 3-D or 4-D (3-D spatial coordinates (x, y, z) and radar azimuth angles θ) scattering power distribution of the imaged scene. Our fundamental assumption is that the imaged scene conforms approximately to a random volume scattering model. Thus, we can use the projection-slice theorem to establish a mathematical relationship between the multi-look interferograms and the scene’s 3-D/4-D scattering power distribution. The fundamental concepts and resolution theory of this new 3-D inversion strategy are developed in 3-D K-space using Fourier aperture synthesis theories. Then, we designed two algorithms for reconstructing a 3-D image: a filtered back-projection algorithm for reconstructing isotropic targets, and a compressed sensing imaging method for reconstructing large-scale targets with anisotropic behaviors. Finally, we verified the feasibility of proposed methods through experiments in real airborne scenarios.