Synthetic Aperture Radar Imaging Algorithm Research Articles

Design of a Near-Field Synthetic Aperture Radar Imaging System Based on Improved RMA

Traditional near-field synthetic aperture radar (SAR) imaging algorithms reveal target features by exploiting signal amplitude and phase information. However, electromagnetic wave propagation is constrained by short distance. Therefore, the spherical wave approximation needs to be considered. In addition, it is also limited by equipment ambient noise, azimuth-distance coupling, wave scattering, and transmission power. Both the amplitude and phase of the signal suffer from the interference of multiple clutter, so they cannot be effectively utilized. To address these issues, this paper introduces a covering penetration detection system based on an improved Range Migration Algorithm (IMRMA) imaging method. Firstly, the proposed method minimizes interferences from the front end of the system using an optimized window to balance denoising and information preservation. Next, interval non-uniform interpolation, instead of Stolt interpolation decoupling, is employed to reduce the computational overhead significantly. To minimize the effects due to wave scattering and propagation loss, distance information is enhanced using amplitude and phase compensation. This reduces scattering effects and enhances image quality. An experimental system is constructed based on a vector network analyzer (VNA) to image the target. The proposed method takes about half the time of traditional RMA. The PSNR in the chunky bowl experiment is higher than 14 dB, which is higher than all the compared methods in the paper. The test results show that the designed system and the reported method can effectively achieve high-resolution images by strengthening the target intensity and suppressing the environmental artifacts.

Increasing SAR Imaging Precision for Burden Surface Profile Jointly Using Low-Rank and Sparsity Priors

The synthetic aperture radar (SAR) imaging technique for a frequency-modulated continuous wave (FMCW) has attracted wide attention in the field of burden surface profile measurement. However, the imaging data are virtually under-sampled due to the severely restricted scan time, which prevents the antenna being exposed to high temperatures and heavy dust in the blast furnace (BF) for an extended period. In traditional SAR imaging algorithm research, the insufficient accumulation of scattered energy in reconstructing the burden surface profile leads to lower imaging precision, and the harsh smelting increases the probability of distortion in shape detection. In this study, to address these challenges, a novel rotating SAR imaging algorithm based on the constructed mechanical swing radar system is proposed. This algorithm is inspired by the low-rank property of the sampled signal matrix and the sparsity of burden surface profile images. First, the sparse FMCW signal is modeled, and the position transform matrix, calculated according to the BF dimensions, is embedded into the dictionary matrix. Then, the low-rank and sparsity priors are considered and reformulated as split variables in order to establish a convex optimization problem. Lastly, the augmented Lagrange multiplier (ALM) is employed to solve this problem under double constraints, and the imaging results are obtained using the alternating direction method of multipliers (ADMM). The experimental results demonstrate that, in the subsequent shape detection, the root mean square error (RMSE) is 15.38% lower than the previous algorithm and 15.63% lower under low signal-to-noise (SNR) conditions. In both enclosed and harsh environments, the proposed algorithm is able to achieve higher imaging precision even under high noise. It will be further optimized for speed and reliability, with plans to extend its application to 3D measurements in the future.

Frequency Domain Imaging Algorithms for Short-Range Synthetic Aperture Radar

In order to achieve miniaturization, short-range radar (SRR) generally adopts millimeter-wave (MMW) radar with a frequency-modulated continuous-wave (FMCW) system, which may make the stop–go–stop assumption in traditional synthetic aperture radar (SAR) imaging algorithms invalid. In addition, in order to observe a large enough area, SRR often needs a wide radar beam, which may cause serious range–azimuth coupling when using SRR for SAR imaging. The above two problems may make the traditional SAR imaging algorithm invalid in SRR SAR imaging. Taking the SRR SAR imaging application into account, traditional frequency domain SAR imaging algorithms are analyzed and improved in this paper. Firstly, the intra-pulse motion (IPM) caused by the FMCW system and the two-dimensional coupling (TDC) in the case of a wide beam are analyzed. Subsequently, the applicability of the range Doppler algorithm (RDA), the frequency scaling algorithm (FSA) and the range migration algorithm (RMA) for SRR SAR is analyzed. Then, improvement measures are put forward to address the aliasing and folding phenomena caused by the wide-beam problem in the FSA and RMA, respectively. Finally, the effectiveness of the proposed algorithm is verified using simulation data and real measured data collected using an MMW radar fixed on a slide rail.

A Grid-Based Gradient Descent Extended Target Clustering Method and Ship Target Inverse Synthetic Aperture Radar Imaging for UHF Radar

Inland shipping is of great significance in economic development, and ship surveillance and classification are of great importance for ship management and dispatch. For river ship detection, ultrahigh-frequency (UHF) radar is an effective equipment owing to its wide coverage and easy deployment. The extension in range, Doppler, and azimuth and target recognition are two main problems in UHF ship detection. Clustering is a necessary step to get the center of an extended target. However, it is difficult to distinguish between different target echoes when they overlap each other in range, Doppler, and azimuth and so far practical methods for extended target recognition with UHF radar have been rarely discussed. In this study, a two-stage target classification method is proposed for UHF radar ship detection. In the first stage, grid-based gradient descent (GBGD) clustering is proposed to distinguish targets with three-dimensional (3D) information. Then in the second stage, the inverse synthetic aperture radar (ISAR) imaging algorithm is employed to differentiate ships of different types. The simulation results show that the proposed method achieves a 20% higher clustering accuracy than other methods when the targets have close 3D information. The feasibility of ISAR imaging for target classification using UHF radar is also validated via simulation. Some experimental results are also given to show the effectiveness of the proposed method.

High Precision Motion Compensation THz-ISAR Imaging Algorithm Based on KT and ME-MN

In recent years, terahertz (THz) radar has been widely researched for its high-resolution imaging. However, the traditional inverse synthetic aperture radar (ISAR) imaging algorithms in the microwave band perform unsatisfactorily in the THz band. Firstly, due to THz radar’s large bandwidth and short wavelength, the rotation of the target will result in serious space-varying(SV) range migration and space-varying phase error. Furthermore, it is challenging to accurately estimate the rotational velocity and compensate for phase errors in the presence of severe range migration effects. Therefore, in this paper, a high-precision THz-ISAR imaging algorithm is proposed. The algorithm includes the following step: First, the SV first-order range migration(FRM) is corrected using keystone transform (KT); then, the minimum entropy based on modified newton (ME-MN) is used to estimate the rotational velocity roughly, and the remaining SV second-order range migration(SRM) is corrected to obtain the range profile with the envelope alignment. Finally, the echo after the envelope alignment is processed for the second time based on ME-MN. The target rotation velocity is accurately estimated, and the phase error is compensated to obtain a well-focused imaging result. The validity of the proposed method is verified by numerical simulation and electromagnetic calculation data.

One-bit SAR imaging algorithm based on MC function and TV norm

Synthetic Aperture Radar (SAR) imaging is generally characterized by a large amount of data and a high sampling rate. The traditional one-bit compressed sensing will generate virtual targets when recovering images at a low SNR, resulting in low reconstruction accuracy and obvious noise impact of the algorithm. In this paper, a one-bit SAR imaging algorithm based on the Minimax Concave (MC) penalty function and the Total Variation (TV) norm is proposed, which can potentially improve the reconstruction accuracy. At the same time, to solve the problem of speckle noise generated in the imaging process, this paper proposes a Fast Low-rank Sparse Decomposition (FLRSD) algorithm based on the one-bit contaminated image, which potentially improves the anti-noise performance and reconstruction efficiency. The results of simulation and measured data show that the proposed algorithms have better reconstruction accuracy and focusing performance than other algorithms. Even at low SNR, they also have good reconstruction effect.

High-Resolution Synthetic Aperture Imaging Algorithm for Terahertz

The reconstruction and recovery of targets in the terahertz band has always been a direction for the improvement of synthetic aperture radar imaging algorithms. In order to improve the imaging resolution of the target, the scattering principle of electromagnetic waves to the target is analyzed. Through model establishment and formula derivation, the propagation process between the electromagnetic wave and the target is obtained, and the simulation and experiment verify that the corresponding algorithm can greatly improve the accuracy of target recovery.

Processing of Airborne Microwave Photonic SAR Raw Data With Inaccurate RSF

Due to system instability and other reasons, the actual range sampling frequency of the system may deviate from the ideal value for the microwave photonic synthetic aperture radar (SAR). This deviation may lead to severe residual range cell migration (RCM) and even range defocus after imaging, which can seriously affect the image quality. To resolve this problem, this paper proposes an airborne microwave photonic SAR imaging algorithm based on inaccurate system parameter estimation. Firstly, the algorithm estimates and compensates for the range spatial-variant motion error to eliminate the effect of this motion error on the remaining RCM and range defocus. Secondly, based on the minimum entropy criterion of the image, we use the optimization model to estimate the actual range sampling frequency (RSF). Finally, the existing wide-beam autofocus method is used to correct the azimuth spatial-variant motion error. The simulation data and the measured data processing results verify the effectiveness of the proposed method.

Synthetic aperture radar images denoising based on multi-scale attention cascade convolutional neural network

Synthetic aperture radar (SAR) images are often affected by speckle noise, which can hinder accurate interpretation and subsequent use of the images in applications such as target detection and segmentation. To address this issue, we propose a denoising algorithm based on a multi-scale attention cascade convolutional neural network (MSAC-Net). Our algorithm employs multi-scale asymmetric convolution to extract image features and an attention mechanism to integrate these features. Additionally, we designed a multi-layer deep cascade convolutional network to enhance the generalization ability of the model features. Experimental results show that our proposed MSAD-Net model significantly outperforms state-of-the-art SAR image denoising algorithms. Specifically, it achieves a significant improvement in peak signal-to-noise ratio, with an increase of about 0.81–13.97 dB, and structural similarity index measure, with an increase of about 0.01–0.14. Overall, our study presents a novel denoising algorithm for SAR images that greatly improves the accuracy of subsequent image applications.

Improving the Probability of Stroke Detection inside the Human Head Using Wideband Polarimetric Synthetic Aperture Radar Imaging

This paper presents an imaging system based on ultrawideband microwave radars for the detection of bleeding regions and strokes inside the human head. The proposed system is portable and focuses on revealing bleeding areas with unpredictable shapes using polarimetric data acquisition. A custom-designed dual-polarized bowtie patch antenna capable of pumping the UWB pulses inside the patient’s head is designed and presented as the biomedical sensor for the system. The antenna dimensions are 25 × 25 m m 2 . It mitigates the mismatch between the air and skin needles of any coupling liquid, resulting in safe SAR levels below 0.5 W/kg and wideband operating bandwidth that covers 1.2-4.5 GHz with unidirectional radiations. To collect the raw polarimetric data, an elliptical array of the proposed antenna is formed around the patient’s head model that includes 16 elements in direct contact with the phantom. The performance of the proposed method is validated through an imaging scenario with two nonuniform bleeding areas inside the patient head model. The whole structure is simulated with a Gaussian pulse as the excitation using CST Microwave Studio tools. To produce 2D images of the voxel model, the time-domain elliptical synthetic aperture radar (ESAR) imaging algorithm is applied. Accurately detecting the presence and shape of anomalies in reconstructed images using the proposed method demonstrates the efficiency of the proposed system and determines its advantages over single-polarization systems.

Multi-Channel Back-Projection Algorithm for mmWave Automotive MIMO SAR Imaging With Doppler-Division Multiplexing

Due to the low-cost and compact size, millimeter-wave radar is gaining increasing attention in automotive applications. Compared with the array imaging technique, synthetic aperture radar (SAR) takes advantage of high resolution, low system complexity, and is an important powerful tool in automotive radar imaging. In this paper, a high-resolution MIMO SAR imaging algorithm is proposed in the framework of Doppler-division multiplexing (DDM), where a novel multi-channel back-projection (BP) approach is presented for coherent multi-channel image integration. In the scheme, the DDM is applied on transmitter and Doppler filtering is employed to separate echoes of different transmitters on one receiver. Then the proposed multi-channel BP algorithm is divided into two parts. The first step corresponds to the BP SAR imaging on each channel, effectively dealing with the problem of near-field and wide-angle focusing. As follows, digital beamforming (DBF) and beampattern synthesis based on alternating direction method of multipliers (ADMM) is used for multi-channel SAR image coherent fusion. Experiments using real-measured data from an automotive radar system with a two-transmitter-eight-receiver array validate the effectiveness of the proposed algorithm.

Back-Projection Imaging for Synthetic Aperture Radar with Topography Occlusion

When synthetic aperture radar (SAR) is conducting remote sensing or terrain mapping, its radar beam is inevitably occluded by the variations in the under-test topography. Although back-projection algorithm (BPA) can theoretically directly solve the imaging problems of topography variations that most current SAR imaging algorithms cannot handle, these BPAs only solve the phase focusing of SAR echo signal, and do not consider the mismatch of SAR imaging results caused by topography occlusion. To solve the mis-imaging issue of the occluded area generated by BPA under the case of topography variation, a topography-based BPA (Topo-BPA) is proposed in this paper. Firstly, a new beam occlusion judgment algorithm based on spherical wave assumption is proposed, and its core is depression angle interpolation and depression angle updating. Then, the proposed Topo-BPA embeds the proposed beam occlusion judgment algorithm before the classical BPA, which not only did not reduce the focus depth of BPA, but improved the imaging accuracy of classical BPA. Finally, numerical experiments have demonstrated the superiority of the Topo-BPA’s performance in comparison with classical BPA.

FPGA Implementation of the Chirp-Scaling Algorithm for Real-Time Synthetic Aperture Radar Imaging.

Synthetic aperture radar (SAR), which can generate images of regions or objects, is an important research area of radar. The chirp scaling algorithm (CSA) is a representative SAR imaging algorithm. The CSA has a simple structure comprising phase compensation and fast Fourier transform (FFT) operations by replacing interpolation for range cell migration correction (RCMC) with phase compensation. However, real-time processing still requires many computations and a long execution time. Therefore, it is necessary to develop a hardware accelerator to improve the speed of algorithm processing. In addition, the demand for a small SAR system that can be mounted on a small aircraft or drone and that satisfies the constraints of area and power consumption is increasing. In this study, we proposed a CSA-based SAR processor that supports FFT and phase compensation operations and presents field-programmable gate array (FPGA)-based implementation results. We also proposed a modified CSA flow that simplifies the traditional CSA flow by changing the order in which the transpose operation occurs. Therefore, the proposed CSA-based SAR processor was designed to be suitable for modified CSA flow. We designed the multiplier for FFT to be shared for phase compensation, thereby achieving area efficiency and simplifying the data flow. The proposed CSA-based SAR processor was implemented on a Xilinx UltraScale+ MPSoC FPGA device and designed using Verilog-HDL. After comparing the execution times of the proposed SAR processor and the ARM cortex-A53 microprocessor, we observed a 136.2-fold increase in speed for the 4096 × 4096-pixel image.

Superresolution Composite SAR Imaging Method by Hierarchical Pattern With Attributed Scattering Priors

Compared with the ideal point scattering model, the attributed scattering center (ASC) model provides concise and physically relevant description of the man-made targets, such as the vehicles and ships, giving an efficient way to interpret the measurements of high-frequency and wide-angle synthetic aperture radar (SAR). Since the ASC model is complicated, it gives rise to a high-dimensional model parameter estimation problem, leading to a heavy computational burden. Aimed at this problem, in this article, a novel superresolution composite SAR imaging algorithm is proposed based on sparse representation, in which the ASC attributes are estimated by a hierarchical pattern for reducing the computational cost. In the proposed algorithm, the ASC model parameters, classified into three categories, are estimated coarsely via minimum variance pursuit (MVP). Then these coarse estimates are refined jointly using the consensus alternating direction method of multipliers (CADMM) optimization for polarimetric measurements. The spectrum extrapolation and replacement are performed for superresolution and reducing the impact of model mismatch, followed by a fast Fourier transform (FFT)-based operation to efficiently obtain the final superresolution image. The proposed approach not only possesses the ability of superresolution reconstruction with the target nonideal scattering features’ enhancement but also offers the physically relevant attributes on the premise of estimation accuracy improvement and calculation capacity reduction. Extensive experiments are conducted to corroborate the effectiveness of the proposed algorithm.

Spaceborne Multichannel SAR Imaging Algorithm for Maritime Moving Targets

Spaceborne multi-channel synthetic aperture radar (SAR) is an effective means to realize high-resolution and wide-swath imaging. However, for spaceborne multi-channel SAR imaging of maritime moving targets, the target motion will cause undesired channel imbalance, i.e., phase error, and further introduce the spurious targets in the image. To solve this problem, this paper proposes a novel spaceborne multi-channel SAR imaging algorithm for maritime moving targets, which consists of sequential coarse imaging and accurate imaging. The key strategies are to separate different moving targets by coarse imaging and to estimate the phase error based on the relationship between phase errors and amplitudes of spurious targets. First, the quantitative relationship between phase errors and amplitudes of spurious targets is established. Second, based on coarse imaging results, different maritime targets are effectively separated. Then, based on the measured amplitudes of spurious targets, a cost function, which represents the difference between the real target velocity and estimated target velocity, is constructed and minimized to separately estimate the velocities and phase errors of targets. Moreover, to further improve the accuracy of estimation and to suppress the undesired effects caused by target defocusing, clutter, and noise, an iterative strategy is adopted. Last, by auto-focusing, a well-focused SAR image is obtained. The GF-3 dual-channel real data experiment is conducted. The results indicate that the spurious targets are well suppressed, which validates the effectiveness of the proposed algorithm.

Learning-Based High-Frame-Rate SAR Imaging

As high-frame-rate synthetic aperture radar (SAR) has the ability to form continuous SAR images and dynamically monitor the ground areas of interest, it has attracted more and more attention nowadays. In practical application, the enormous data in high-frame-rate SAR system to obtain the multi-frame images brings big challenges to its transmission, storage, and processing. In order to solve this problem, there are many recent papers on formulating the high-frame-rate SAR imaging problem into a low-rank tensor recovery problem, and correspondingly, the sampling amount of the high-frame-rate SAR data can be largely reduced. However, existing algorithms to solve the low-rank tensor recovery problem in high-frame-rate SAR application still suffer from large computational cost. Under the above inspiration, this paper proposes a deep neural network architecture for high-frame-rate SAR imaging, i.e., Tensor Alternating Direction Method of Multiplier Network (TADMM-Net), which is more computational efficient in imaging procedure. Specifically, we formulate the high-frame-rate SAR imaging processing into a low-tubal-rank tensor recovery problem. We solve the low-tubal-rank tensor recovery problem using a tensor Alternating Direction Method of Multiplier (ADMM) algorithm and then design a new deep neural network architecture by applying algorithm unfolding techniques to the underlying low-rank tensor recovery problem. The proposed TADMM-Net approach shifts the computational burden from the testing phase to the training phase, and the practical processing time can be extremely decreased compared with the existing algorithms for the low-rank tensor recovery problem in high-frame-rate SAR imaging application. It also offers various advantages over the existing high-frame-rate SAR imaging algorithms including higher performance in the case with low sampling amount, lower storage complexity and no requirement for hand-craft hyper-parameters adjustment. The methodology was tested on high-frame-rate SAR data. These tests show that the proposed architecture outperforms other state-of-the-art methods in high-frame-rate SAR imaging applications.

Mission Design and Orbit-Attitude Control Algorithms Development of Multistatic SAR Satellites for Very-High-Resolution Stripmap Imaging

This study designs a multistatic synthetic aperture radar (SAR) formation-flying system for very-high-resolution stripmap imaging (VHRSI) using manufacturable SAR microsatellites. Multistatic SAR formation specifications for VHRSI are derived based on the SAR image theory. For the simultaneous multi-satellite operation, the advantages of the autonomous orbit and attitude control are prominent in terms of the workload of the ground station or the efficient performance of missions. Therefore, the autonomous relative-orbit-control algorithm using relative orbital elements is developed to maintain the designed multistatic SAR formation. Additionally, an autonomous attitude-control algorithm for multistatic SAR imaging is designed by applying the optimal right-ascension of the descending node (RADN) sector concept. Finally, the resolution improvement of VHRSI is verified through multistatic SAR imaging simulations. The multistatic SAR formation is designed with three satellites separated by 7.5 km each in the along-track direction. Autonomous relative orbit control maintains the relative position error within 45 m (3σ). Additionally, the autonomous attitude control simulation verifies that the satellites perform attitude maneuvers suitable for the operation mode, and the pointing error is maintained within 0.0035° (3σ). The spatial resolution of the multistatic SAR system for VHRSI is 0.95 × 0.96 m, which satisfies the very-high-spatial-resolution requirement.

Resolution Enhancement for Millimeter-Wave Radar ROI Image with Bayesian Compressive Sensing.

For millimeter-wave (MMW) imaging security systems, the image resolution promisingly determines the performance of suspicious target detection and recognition. Conventional synthetic aperture radar (SAR) imaging algorithms only provide limited resolution in active MMW imaging, which is limited by the system. In terms of enhancing the resolution of a region of interest (ROI) image containing suspicious targets, super-resolution (SR) imaging is adopted via Bayesian compressive sensing (BCS) implemented by fast Fourier transform (FFT). The spatial sparsity of MMW ROI images is well exploited with BCS to achieve resolution enhancement without computational cost. Both simulated and measured experiments confirm that the proposed scheme effectively improves the resolution of ROI images.

An Efficient Backprojection Algorithm Based on Wavenumber-Domain Spectral Splicing for Monostatic and Bistatic SAR Configurations

This paper introduces a fast backprojection synthetic aperture radar (SAR) imaging algorithm based on wavenumber-domain spectral splicing. The traditional fast backprojection (FBP) algorithm establishes the polar coordinate system with the center of the sub-aperture as the origin. Therefore, the coordinates of the image obtained from each sub-aperture are different. Sub-aperture images must be projected to a uniform coordinate system before they can be coherently superimposed to form the final image, which requires a large amount of calculation. In order to deal with this problem, this paper proposes a novel imaging method, which uses the same polar coordinate system for each sub-aperture. The sub-aperture images are then spliced in the wavenumber-domain, and directly added after upsampling. This method avoids the projection from each sub-aperture to the uniform coordinate system, thus improving the imaging accuracy and efficiency. At the same time, the algorithm is suitable for various configurations, and can achieve good imaging results for bistatic forward-looking SAR and high-speed mobile platform. Finally, simulations are presented to demonstrate the effectiveness of the algorithm.

Land cover classification of synthetic aperture radar images based on encoder--decoder network with an attention mechanism

More and more high-resolution synthetic aperture radar (SAR) image datasets have been available, which promote the applications of SAR images in land cover classification, such as vegetation monitoring, land cover, land use, cartography, etc. A novel algorithm based on the encoder-decoder structure is proposed in this paper. We add the channel attention and spatial attention modules to the algorithm for SAR images land cover classification. And these two modules, which interact with each other instead of being completely independent, can maximize the use of extracted information. Experiments on the high-resolution Gaofen-3 dataset have been carried out. Some classical semantic segmentation algorithms, including SegNet, HR-Net, Deeplabv3+, and GF3-baseline, are utilized to compare with the proposed algorithm. The experimental results demonstrate that the encoder–decoder network with the attention mechanism can get higher accuracy than other encoder–decoder networks.