MiniSAR System Research Articles

First Demonstration of Metamaterial-Modulated Tag Localization and Imaging Based on K-Band FMCW Mini-SAR System

First Demonstration of Metamaterial-Modulated Tag Localization and Imaging Based on <i>K</i>-Band FMCW Mini-SAR System

Recognition for SAR deformation military target from a new MiniSAR dataset using multi-view joint transformer approach

Accurately detecting ground armored weapons is crucial for achieving initiative advantages in military operations. Generally, satellite or airborne synthetic aperture radar (SAR) systems face limitations due to their revisit cycles and fixed flight trajectories, resulting in single-view imaging of targets, thereby hampering the recognition of small SAR ground targets. In contrast, MiniSAR possesses the capability to capture the multi-view of a target by acquiring images from different azimuth angles. In this research, our team utilizes a self-developed MiniSAR system to generate multi-view SAR images of real ground armored targets and recognize targets. However, the recognition of small targets in SAR images encounters two significant difficulties. First, small targets in SAR images are prone to interference from background noise. Second, SAR target deformation arises from variations in depression angles and imaging processes. To tackle these difficulties, this paper proposes a novel SAR ground deformation target recognition approach based on a joint multi-view transformer model. The method first preprocesses SAR images based on a low-frequency priori SAR image denoising method. Next, it obtains multi-view joint information through a self-attentive mechanism, inputs joint features to the transformer structure. The outputs are jointly updated by a multi-way averaging adaptive loss function to improve the recognition accuracy of deformed targets. The experimental results demonstrate the superiority of the proposed method in SAR ground deformation target recognition, outperforming other representative approaches such as information fusion of target and shadow (IFTS) and Vision Transformer (ViT). It is concluded that the proposed method has high recognition accuracies of 98.37% and 93.86 % on the moving and stationary target acquisition and recognition (Mstar) and our SAR images dataset, respectively, in the field of SAR ground deformation target recognition. We have included links to the code and data in the abstract of this paper for ease of access. The source code and sample dataset are available at https://github.com/Lvjiming/MJT.

Target Recognition in SAR Images by Deep Learning with Training Data Augmentation.

Mass production of high-quality synthetic SAR training imagery is essential for boosting the performance of deep-learning (DL)-based SAR automatic target recognition (ATR) algorithms in an open-world environment. To address this problem, we exploit both the widely used Moving and Stationary Target Acquisition and Recognition (MSTAR) SAR dataset and the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset, which consists of selected samples from the MSTAR dataset and their computer-generated synthetic counterparts. A series of data augmentation experiments are carried out. First, the sparsity of the scattering centers of the targets is exploited for new target pose synthesis. Additionally, training data with various clutter backgrounds are synthesized via clutter transfer, so that the neural networks are better prepared to cope with background changes in the test samples. To effectively augment the synthetic SAR imagery in the SAMPLE dataset, a novel contrast-based data augmentation technique is proposed. To improve the robustness of neural networks against out-of-distribution (OOD) samples, the SAR images of ground military vehicles collected by the self-developed MiniSAR system are used as the training data for the adversarial outlier exposure procedure. Simulation results show that the proposed data augmentation methods are effective in improving both the target classification accuracy and the OOD detection performance. The purpose of this work is to establish the foundation for large-scale, open-field implementation of DL-based SAR-ATR systems, which is not only of great value in the sense of theoretical research, but is also potentially meaningful in the aspect of military application.

Segmental Aperture Imaging Algorithm for Multirotor UAV-Borne MiniSAR

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.

Moving Targets Detection for Video SAR Surveillance Using Multilevel Attention Network Based on Shallow Feature Module

In this article, a novel method for the moving target detection through multilevel spatial and channelwise attention network based on shallow feature channel module (MSCA-SFCM) is presented, and the circular spotlight (CSL) video synthetic aperture radar ground moving target indication (Video-SAR-GMTI) mode of the Nanjing University of Aeronautics and Astronautics miniature SAR (NUAA MiniSAR) system is introduced. However, due to the lack of moving target samples, MSCA-SFCM cannot be directly applied to the CSL Video-SAR-GMTI mode in the real system. To this end, this article proposes a training sample library construction scheme for moving targets of high verisimilitude. In this scheme, based on the radar system parameters, after the traversal of moving target parameters and SAR imaging, the scattering line characteristic of all possible moving targets under the current system parameters is simulated and then used for MSCA-SFCM network training. Afterward, the properly trained network can be used for moving target detection in real radar data. The effectiveness of the proposed method is verified by the NUAA MiniSAR system.

Demonstration of Simultaneous Localization and Imaging With Multirotor-Borne MiniSAR

This paper extends the segmental aperture imaging (SAI) algorithm &#x005B;28&#x005D; which tackles the challenges of synthetic aperture radar (SAR) imaging of miniaturized synthetic aperture radar (MiniSAR) onboard multi-rotor unmanned aerial vehicle (UAV). The SAI algorithm is developed based on an accurate phase error model of both translational and rotational motion of UAV. It does require auxiliary positioning data such as GPS&#x002F;INS. We further extend it to simultaneously estimate and reconstruct the 3D trajectory of UAV from parameters derived from SAI motion compensation. Thus, it in fact accomplishes the simultaneously location and imaging (SLAI) using UAV-borne radar. It is first validated and evaluated via raw signal simulation using realistic trajectory data. For experiment purpose, a multirotor-borne MiniSAR system FUSAR-Ku is developed. Experimental results show that the proposed algorithm can achieve decimeter-resolution imaging performance as measured by various metrics, while simultaneously accomplishing centimeter-to-decimeter 3D self-positioning capability. It is a first demonstration of SLAI mode with a multirotor-borne MiniSAR.

Effects of Motion Compensation Residual Error and Polarization Distortion on UAV-Borne PolInSAR

Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) can improve interferometric coherence and phase quality, which has good application potential. With the development of the Mini-SAR system, Unmanned Aerial Vehicle borne (UAV-borne) PolInSAR systems became a reality. However, UAV-borne PolInSAR is easily affected by air currents and other factors, which may cause large motion errors and polarization distortion inevitably exists. However, there are few pieces of research which are about motion compensation residual error (MCRE) and polarization distortion effects on PolInSAR. Though the effects of MCRE on Interferometric SAR (InSAR) and polarization distortion on PolInSAR were studied, respectively, these two parts are independently modeled and analyzed. In this paper, a model that simultaneously considers the effects of these two kinds of errors is proposed, and simulation results are given to validate the model. Then, a quantitative analysis based on a real Quadcopter UAV PolInSAR system is performed according to the model, which is valuable for system design and practical application of the UAV-borne PolInSAR system.

Multirotors Video Synthetic Aperture Radar: System Development and Signal Processing

360-degree video synthetic aperture radar (VideoSAR) presents a powerful capability for the information perception of dynamic scenes. In comparison to time-consuming airborne SAR, miniature SAR (MiniSAR) mounted on an unmanned aerial vehicle platform is a cost-effective configuration that provides a higher operation flexibility for video acquisition. Nevertheless, MiniSAR system development and signal processing limited by multiple factors are still challenging issues, including restricted weight, volume, power consumption, complicated trajectory imaging, and high-efficient video processing. In this article, we develop a multirotors VideoSAR system at X-band in Nanjing University of Aeronautics and Astronautics, concerning hardware architecture, field experiment, and video signal processing. It has the capability of ultrahigh resolution imaging with three available channels up to 1.8-GHz transmission bandwidth. Field programmable gate array-based unified signal processing architecture can further accelerate the generation of massive VideoSAR sequences in terms of both circular spotlight and stripmap modes. Several experimental results have been presented to demonstrate the validity of our developed system.

A W-Band 3-D Integrated Mini-SAR System With High Imaging Resolution on UAV Platform

The light-small multi-rotor unmanned aerial vehicle (UAV) carrying with a mini synthetic aperture radar (SAR) payload has provided an advanced information acquisition technology for the target detecting and imaging fields. In this paper, a W-band compact SAR system is proposed, which has been successfully deployed on the unmanned aerial vehicle (UAV) platform demonstrating state-of-the-art imaging resolution of about 4.5 cm. In order to achieve remarkable compactness, light weight and powersaving feature for the proposed system, the techniques of the three-dimensional (3-D) integration are adopted, where a whole-system size of only 67 × 60×50 mm 3 and the weight of 400 g are obtained. This proposed system achieves higher SAR-imaging resolution thanks to the contributions of many efforts, including the technique of high-linearity waveform generation, the substrate integrated waveguide (SIW) antenna with high isolation by the electromagnetic band-gap (EBG) mechanism, and a novel motion compensation method for the imaging formation. To validate this proposed idea, a UAV SAR prototype operating at W-band is designed and fabricated. The measurements show great signal-to-noise ratio imaging results with excellent focusing effect. The proposed SAR system is promisingly an ideal candidate for the target detecting/imaging applications deployed on the UAV platform.

Residual RCM Correction for LFM-CW Mini-SAR System Based on Fast-Time Split-Band Signal Interferometry

A linear frequency modulation continuous-wave mini-synthetic aperture radar (SAR) system mounted on small aircrafts promises a high-flexibility and cost-effective microwave remote sensing technology. However, the mini-SAR system suffers from considerable trajectory deviations due to aircraft’s lightweight, low flight height, and limited capacity for a high-accuracy inertial measurement unit (IMU). With the rapid increasing requirements for resolution, the residual range cell migration (RCM) exceeds a single range cell. Under such circumstances, traditional autofocus algorithms fail to guarantee well-focused mini-SAR images and further accurate interferometric SAR (InSAR) applications. To solve these problems, this paper proposed a novel residual RCM correction scheme for a mini-SAR system mounted on small aircrafts without high-accuracy IMU. The core idea is to estimate the misalignments of adjacent range profiles based on fast-time split-band signal interferometry at each azimuth time and integrate them with time to obtain an estimation of residual RCM. The proposed method promises a high-accuracy misalignment estimation result without resampling operation in the traditional cross-correlation methods. Simulation and experimental results show the improvement of mini-SAR image focusing quality and refinement of coherence map between the master and slave images for InSAR applications, which demonstrated the effectiveness and reliability of our proposed residual RCM correction scheme for the mini-SAR system.