3D Synthetic Aperture Radar Imaging Research Articles

A Novel Multimodal Fusion Framework Based on Point Cloud Registration for Near-Field 3D SAR Perception

This study introduces a pioneering multimodal fusion framework to enhance near-field 3D Synthetic Aperture Radar (SAR) imaging, crucial for applications like radar cross-section measurement and concealed object detection. Traditional near-field 3D SAR imaging struggles with issues like target–background confusion due to clutter and multipath interference, shape distortion from high sidelobes, and lack of color and texture information, all of which impede effective target recognition and scattering diagnosis. The proposed approach presents the first known application of multimodal fusion in near-field 3D SAR imaging, integrating LiDAR and optical camera data to overcome its inherent limitations. The framework comprises data preprocessing, point cloud registration, and data fusion, where registration between multi-sensor data is the core of effective integration. Recognizing the inadequacy of traditional registration methods in handling varying data formats, noise, and resolution differences, particularly between near-field 3D SAR and other sensors, this work introduces a novel three-stage registration process to effectively address these challenges. First, the approach designs a structure–intensity-constrained centroid distance detector, enabling key point extraction that reduces heterogeneity and accelerates the process. Second, a sample consensus initial alignment algorithm with SHOT features and geometric relationship constraints is proposed for enhanced coarse registration. Finally, the fine registration phase employs adaptive thresholding in the iterative closest point algorithm for precise and efficient data alignment. Both visual and quantitative analyses of measured data demonstrate the effectiveness of our method. The experimental results show significant improvements in registration accuracy and efficiency, laying the groundwork for future multimodal fusion advancements in near-field 3D SAR imaging.

A SAR Image-Despeckling Method Based on HOSVD Using Tensor Patches

Coherent imaging systems, such as synthetic aperture radar (SAR), often suffer from granular speckle noise due to inherent defects, which can make interpretation challenging. Although numerous despeckling methods have been proposed in the past three decades, SAR image despeckling remains a challenging task. With the extensive use of non-local self-similarity, despeckling methods under the non-local framework have become increasingly mature. However, effectively utilizing patch similarities remains a key problem in SAR image despeckling. This paper proposes a three-dimensional (3D) SAR image despeckling method based on searching for similar patches and applying the high-order singular value decomposition (HOSVD) theory to better utilize the high-dimensional information of similar patches. Specifically, the proposed method extends two-dimensional (2D) to 3D for SAR image despeckling using tensor patches. A new, non-local similar patch-searching measure criterion is used to classify the patches, and similar patches are stacked into 3D tensors. Lastly, the iterative adaptive weighted tensor cyclic approximation is used for SAR image despeckling based on the HOSVD method. Experimental results demonstrate that the proposed method not only effectively reduces speckle noise but also preserves fine details.

Leveraging joint sparsity in 3D synthetic aperture radar imaging

Leveraging joint sparsity in 3D synthetic aperture radar imaging

3D THz-TDS SAR Imaging by an Inverse Synthetic Cylindrical Aperture

Terahertz time-domain spectroscopy (THz-TDS) is a promising tool for high-resolution 3D imaging of objects due to the high center frequency and bandwidth compared to microwave systems. In addition, terahertz waves have a higher penetration depth than visible or near-infrared radiation. Typically, optics are used to focus the terahertz radiation onto an object under test. This limits the imaging capability in the axial dimension to the depth of field and limits simple imaging of complex surfaces. In this work, we adapt a backpropagation algorithm from synthetic aperture radar (SAR) imaging to reconstruct high-resolution 3D images from time-domain traces acquired with a lensless THz-TDS system. For this purpose, an inverse cylindrical aperture is used and an equation that describes the maximum achievable resolution as a function of the beam pattern, the bandwidth, and the length of the synthetic aperture is derived. The calculated resolution is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$960 \mu \text{m}$ </tex-math></inline-formula> for the linear dimension of the aperture and 75 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> for the rotationally symmetric dimension of the cylindrical aperture for a bandwidth of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\,\text {THz}$ </tex-math></inline-formula> . The resolving power is verified by measurements on metallic and dielectric objects. In the future, this method can be used for non-destructive testing of objects with complex shaped surfaces and internal structures.

Drone-Based 3D Synthetic Aperture Radar Imaging with Trajectory Optimization.

This paper presents a trajectory determination and optimization method of multirotors equipped with a single-channel radar to obtain 3D Synthetic Aperture Radar imaging. The result is a realistic trajectory that allows to obtain an imaging of the assumed quality in less time than using a multi-pass trajectory. The optimization criteria, in addition to the cross-range resolution, are the Peak Sidelobe Ratio (PSLR), Integrated Sidelobe Ratio (ISLR), and time of flight. The algorithm is based on a realistic motion model of the radar platform. This paper presents all the steps of the algorithm and provides simulation results that show its practical applicability. The advantage of the presented approach over the existing ones is indicated and further research directions are proposed.

3D Sparse SAR Image Reconstruction Based on Cauchy Penalty and Convex Optimization

Three-dimensional (3D) synthetic aperture radar (SAR) images can provide comprehensive 3D spatial information for environmental monitoring, high dimensional mapping and radar cross sectional (RCS) measurement. However, the SAR image obtained by the traditional matched filtering (MF) method has a high sidelobe and is easily disturbed by noise. In order to obtain high-quality 3D SAR images, sparse signal processing has been used in SAR imaging in recent years. However, the typical L1 regularization model is a biased estimation, which tends to underestimate the target intensity. Therefore, in this article, we present a 3D sparse SAR image reconstruction method combining the Cauchy penalty and improved alternating direction method of multipliers (ADMM). The Cauchy penalty is a non-convex penalty function, which can estimate the target intensity more accurately than L1. At the same time, the objective function maintains convexity via the convex non-convex (CNC) strategy. Compared with L1 regularization, the proposed method can reconstruct the image more accurately and improve the image quality. Finally, three indexes suitable for SAR images are used to evaluate the performance of the method under different conditions. Simulation and experimental results verify the effectiveness of the proposed method.

Three-Dimensional Sparse SAR Imaging with Generalized Lq Regularization

Three-dimensional (3D) synthetic aperture radar (SAR) imaging provides complete 3D spatial information, which has been used in environmental monitoring in recent years. Compared with matched filtering (MF) algorithms, the regularization technique can improve image quality. However, due to the substantial computational cost, the existing observation-matrix-based sparse imaging algorithm is difficult to apply to large-scene and 3D reconstructions. Therefore, in this paper, novel 3D sparse reconstruction algorithms with generalized Lq-regularization are proposed. First, we combine majorization–minimization (MM) and L1 regularization (MM-L1) to improve SAR image quality. Next, we combine MM and L1/2 regularization (MM-L1/2) to achieve high-quality 3D images. Then, we present the algorithm which combines MM and L0 regularization (MM-L0) to obtain 3D images. Finally, we present a generalized MM-Lq algorithm (GMM-Lq) for sparse SAR imaging problems with arbitrary q0≤q≤1 values. The proposed algorithm can improve the performance of 3D SAR images, compared with existing regularization techniques, and effectively reduce the amount of calculation needed. Additionally, the reconstructed complex image retains the phase information, which makes the reconstructed SAR image still suitable for interferometry applications. Simulation and experimental results verify the effectiveness of the algorithms.

Near-Field 3D SAR Imaging Using a Scanning Linear MIMO Array With Arbitrary Topologies

For near-field synthetic aperture radar (SAR) imaging, frequency domain imaging algorithms typically require the array elements to be uniformly arranged to facilitate the application of fast Fourier transform (FFT) operations; this greatly restricts the array types that can be used for practical applications. For arrays with non-uniformly positioned antennas, no general and efficient imaging algorithm is available yet. To address this issue, an effective algorithm for efficient three-dimensional (3D) SAR imaging with arbitrary linear multi-input-multi-output (MIMO) array topologies is proposed. Specifically, non-uniform FFT (NUFFT) technology is utilized to tackle the non-uniform sampling issues in both the spatial and the wavenumber domain. Despite the intuitive idea, to achieve satisfied imaging, some issues are non-trivial and should be carefully addressed. Both simulations and experiments show the superiority of the proposed algorithm on computational complexity while maintain the same high imaging precision compared with the back-projection algorithm (BPA). Moreover, we suggest that the concept of NUFFT-based imaging can also be generalized to other imaging regimes with arbitrary array configurations.

Autonomous Airborne 3D SAR Imaging System for Subsurface Sensing: UWB-GPR on Board a UAV for Landmine and IED Detection

This work presents an enhanced autonomous airborne Synthetic Aperture Radar (SAR) imaging system able to provide full 3D radar images from the subsurface. The proposed prototype and methodology allow the safe detection of both metallic and non-metallic buried targets even in difficult-to-access scenarios without interacting with the ground. Thus, they are particularly suitable for detecting dangerous targets, such as landmines and Improvised Explosive Devices (IEDs). The prototype is mainly composed by an Ultra-Wide-Band (UWB) radar module working from Ultra-High-Frequency (UHF) band and a high accuracy dual-band Real Time Kinematic (RTK) positioning system mounted on board an Unmanned Aerial Vehicle (UAV). The UAV autonomously flies over the region of interest, gathering radar measurements. These measurements are accurately geo-referred so as to enable their coherent combination to obtain a well-focused SAR image. Improvements in the processing chain are also presented in order to deal with some issues associated to UAV-based measurements (such as non-uniform acquisition grids) as well as to enhance the resolution and the signal to clutter ratio of the image. Both the prototype and the methodology were validated with measurements, showing their capability to provide high-resolution 3D SAR images.

3D SAR Image Background Separation Based on Seeded Region Growing

The environmental interference and the noise in 3D synthetic aperture radar (SAR) image, considered as the background, are inescapable and ought to be eliminated. For 3D SAR image, there is a spatial separation of the target and the background. Therefore, it is possible to achieve the separation of the target and the background by image segmentation. Due to the complexity of the target shape and the large dynamic range of the SAR image, the background cannot be accurately separated through the amplitude information alone. In this paper, a method based on region growing is proposed to achieve 3D SAR image background separation utilizing the plural and the spatial information. The image enhancement matrix, constructed by the plural information of the SAR image, is implemented to improve the contrast of the image. The seeds are extracted by the weighted Otsu, and the weight is determined by the structure and amplitude information from the target. For the region growing, the growing process is achieved by the accumulation of the growing rate, which can suppress the growing of the noise. During the region growing, the stopping growing condition of each seed is independent and controlled by the seed threshold. The global threshold constrains the almost unrestricted growing of the seed whose amplitude is close to the noise amplitude. The results of the simulation and the experiments verify the performance of the proposed method is higher than that of the compared methods with three image evaluation criteria. Besides, we discuss the cost of computation and the influences of three important parameters to achieve a complete analysis.

Three Dimensional Image-Based Radar Cross Section Extrapolation via Planar Projective Transforms

Conventional radar cross section (RCS) measurements require far-field or compact-antenna-test-range (CATR) conditions, which have strict restrictions and high implementation costs. By contrast, RCS extrapolation methods in the near zone are more convenient and practical, which become the research emphasis in recent years. This paper presents a novel RCS extrapolation method based on the combination of near-field 3D synthetic aperture radar (SAR) technique and planar projective transforms (PPT) algorithm. Firstly, to extract the target’s reflectivity distribution, we apply near-field 3D SAR imaging and obtain 3D near-field RCS (NFRCS) images. Secondly, to meet the requirement of RCS measurements, we derive a novel correction factor used to precisely expand 3D NFRCS images. Finally, we extrapolate the plane-wave responses in the azimuth-vertical dimensions, which presents as planar patterns. The proposed method utilizes the complete 3D image-based information to overcome the inherent constraints of classical RCS extrapolation methods on application scenarios, which has the advantages of broad applicability and high flexibility. The detailed derivation and implementation of this method are described in this paper. The simulation and experiment results verify that the proposed method can provide the equivalent CATR result within ±11° azimuth and elevation angles, and it is applicable to precisely measuring the point, surface, and complex scatterers.

Near-field 3D SAR Imaging Techniques Using a Scanning MIMO Array

Near-field 3-D imaging based on a scanning array is an important application of Synthetic Aperture Radar (SAR) 3-D imaging technology for civil use. Compared with Single-Input-Single-Output (SISO) arrays, a Multi-Input-Multi-Output (MIMO) scanning system is a special imaging method characterized by high imaging quality, high array efficiency, loose requirements for antenna spacing and low cost. In this paper, two imaging regimes, namely, MIMO-planar scanning and MIMO cylindrical scanning, are described in terms of signal models, imaging algorithms, experimental systems, and imaging results. The results show the great application potential of the imaging technology in various scenarios.

Three dimensional SAR imaging based on vortex electromagnetic waves

ABSTRACTA novel three dimensional (3D) synthetic aperture radar (SAR) imaging method based on vortex electromagnetic (EM) wave is proposed in this letter. Compared with the traditional two dimensional (2D) SAR imaging, the proposed method has additional resolution in azimuth angle domain by using the orbital angular momentum (OAM) carried by the vortex EM wave. Compared with the 2D radar imaging using vortex EM wave, the presented method retains the resolution in along-track direction. First, the signal model is discussed, followed by the proposed 3D imaging algorithm. For slant range and along-track directions, the traditional 2D SAR imaging methods are utilized. In order to realize azimuth angle imaging, the Doppler cell migration correction method is presented to decouple the Doppler frequency and the OAM mode. Then, the fast Fourier transformation (FFT) method can be applied to obtain the azimuth angle profile of the target. Finally, according to the imaging geometry, 3D SAR image in other coordinates can be obtained.

Sparse coding of 2D-slice Zernike moments for SAR ATR

ABSTRACTIn this article, a new type of feature, named two-dimensional (2D)-slice Zernike moments, is proposed for synthetic aperture radar (SAR) automatic target recognition (ATR). Target features play an extremely important role in the ATR system. Pixels with different scattering intensities distribute in different positions in SAR images, which represent target inherent signatures determined by the target’s characteristics, including global structure and local details. To extract these various scattering signatures, we developed a feature extraction technique named 2D-slice Zernike moments (2DS-ZMS), which can capture target global and local scattering field distribution information. First, the multilayer 2D-slices of a SAR image are extracted by uniformly cutting the 3D display SAR image along the amplitude direction. Then Zernike moments of each 2D-slice are calculated. Finally, the 2DS-ZMS of the SAR image are formulated into a column vector, called the feature vector. The obtained feature vectors of the targets are fed into a newly developed classifier, i.e. the sparse representation-based classifier (SRC). By projecting the testing sample feature vector on the dictionary made up of training samples feature vectors, the sparse coefficients are solved. The minimum reconstruction residual is adopted as the judgement criterion for predicting the test sample’s class label. Experiments on the moving and stationary target acquisition and recognition (MSTAR) data set validate the effectiveness and superiority of the proposed method.

Spaceborne SIMO-SAR for three-dimensional ionospheric irregularity sounding

Ionospheric irregularity sounding is of significant importance in compensating for ionospheric scintillation effects on satellite communication, navigation, and remote sensing systems at lower frequencies, i.e., L band, P band, etc. A novel spaceborne nadir-looking single input and multiple outputs synthetic aperture radar (SAR) scheme is proposed for three-dimensional (3D) sounding of ionospheric irregularities. The 3D radar echo signal model is developed by examining the ionospheric effects on electromagnetic radar waveforms. These echoes can be used to produce 3D global maps of the distribution of ionospheric irregularities by means of a novel 3D SAR image formation algorithm. The effectiveness of the proposed SAR system was verified by means of computer simulations. The simulation results show that the proposed system has the capability to generate a 3D high-precision image of ionospheric irregularities with an altitude resolution as high as 0.9 km, an along-track resolution as high as 0.4 km, and an across-track angular resolution of 0.25.