The simulation of high-resolution synthetic aperture radar (SAR) imagery in intricate environments remains a formidable challenge. Advancements in reversible microwave-domain surface scattering models are crucial, potentially revolutionizing the fidelity of SAR simulations and streamlining the extraction of target parameters. Drawing inspiration from computer graphics, this article proposes a novel differentiable ray tracing (DRT) approach for microwave rendering and fast SAR imaging. The rendering model utilizes coherent spatially varying (SV) bidirectional scattering distribution function (CSVBSDF) based on the Kirchhoff approximation (KA) and the small perturbation method (SPM), corresponding to specular and diffuse scattering contributions, respectively. SAR imaging is efficiently executed via a fusion of ray tracing (RT) and rapid mapping projection. The innovative DRT reversible engine enables swift estimation of SAR image parameter gradients for direct CSVBSDF surface scattering parameter optimization. The method’s validity is confirmed through comparative analysis with measured SAR images and other methods, demonstrating marked improvements in SAR simulation fidelity across diverse observational scenarios by learning surface scattering parameters.

This article takes on the challenges of synthetic aperture radar (SAR) imaging for miniaturized SAR (MiniSAR) onboard a multirotor unmanned aerial vehicle (UAV). Several unique challenges are systematically analyzed, and a corresponding analytical phase error model is established, which accurately models the effects of both translational and rotational motions of UAVs. A segmental aperture imaging (SAI) algorithm, an autofocus algorithm based on strong scatterers, is proposed. It simply divides the platform trajectory into uneven segments, which are first independently focused with motion compensation and then stitched together to form a complete SAR image. Both the theoretical derivation of the signal model and the implementation of the imaging algorithm are presented. A simulation analysis with actual UAV trajectory and attitude data is conducted, which demonstrates the efficacy and performance of the proposed imaging algorithm. It shows that the ideal focusing effect can be achieved as evaluated by various metrics, and the proposed algorithm has superior performance compared to the subaperture phase gradient autofocus (PGA) and minimum entropy autofocus (MEA) methods. Finally, the multirotor-borne MiniSAR system FUSAR-Ku is used for experiments to verify the proposed algorithm. Experimental results show that the proposed algorithm can achieve the theoretical decimeter-resolution imaging performance as measured by various metrics.

This paper addresses the unique motion errors that must be compensated for miniaturized synthetic aperture radar (MiniSAR) onboard multi-rotor unmanned aerial vehicle (UAV). Segmental aperture imaging (SAI) algorithm is proposed which simply divides the platform trajectory into segments. Each segment is first independently focused with motion compensation and then stitched together to form a complete SAR image. A real-time SAI algorithm implementation is proposed. It performs overlapping subaperture for each segment and performs polynomial fitting of its phase. It is implemented on graphics processing unit (GPU). Finally, the data obtained by the FuSaR-Ku system is used to verify the effectiveness of the real-time SAI algorithm.

This article proposes a MiniSAR imaging algorithm applied to small maneuvering platforms such as multi-rotor UAVs. Due to its load and size limitations, it can only carry low-precision IMU and GPS. These devices are not enough to accurately obtain the motion error of the platform, and thus cannot guarantee the successful imaging of each flight. In this article, an imaging algorithm segment aperture imaging (SAI) algorithm based on time-domain segmentation is designed according to the motion characteristics of a small multi-rotor platform, and the deviation is compensated according to the echo estimation, and then segment stitching is performed to obtain better imaging results. The proposed algorithm is experimentally demonstrated with the Multirotors FUSAR-Ku MiniSAR system.

This article presents a robust hand-gesture recognition method via multidimensional feature representation and learning specifically designed for commercial frequency-modulated continuous wave (FMCW) multi-input multi-output (MIMO) millimeter-wave radar. First, the optimal configuration of the radar system parameters for the hand-gesture recognition scenario is investigated and a standard procedure to determine the system configuration is given. Then a moving scattering center model is proposed to represent the 3-D point cloud in the range-Doppler (RD)-angular multidimensional feature space. A scattering point detection and tracking algorithm is presented based on a set of motion constraints in terms of position, velocity, and acceleration. It is derived from the space-time continuity of a nonrigid target. Finally, a lightweight multichannel convolutional neural network (CNN) is designed to learn and classify multidimensional gesture features including radial RD and tangential azimuth-elevation. Extensive experiments are carried out with the developed system and a large data set is obtained to train and test the classifier. The results show that the proposed gesture recognition method can effectively distinguish gestures that are easily confused in the RD domain and achieve robust performances under various conditions.