Traditional Imaging Algorithms Research Articles

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.

Migration through Resolution Cell Correction and Sparse Aperture ISAR Imaging for Maneuvering Target Based on Whale Optimization Algorithm-Fast Iterative Shrinkage Thresholding Algorithm.

Targets faced by inverse synthetic aperture radar (ISAR) are often non-cooperative, with target maneuvering being the main manifestation of this non-cooperation. Maneuvers cause ISAR imaging results to be severely defocused, which can create huge difficulties in target identification. In addition, as the ISAR bandwidth continues to increase, the impact of migration through resolution cells (MTRC) on imaging results becomes more significant. Target non-cooperation may also result in sparse aperture, leading to the failure of traditional ISAR imaging algorithms. Therefore, this paper proposes an algorithm to realize MTRC correction and sparse aperture ISAR imaging for maneuvering targets simultaneously named whale optimization algorithm-fast iterative shrinkage thresholding algorithm (WOA-FISTA). In this algorithm, FISTA is used to perform MTRC correction and sparse aperture ISAR imaging efficiently and WOA is adopted to estimate the rotational parameter to eliminate the effects of maneuvering on imaging results. Experimental results based on simulation and measured datasets prove that the proposed algorithm implements sparse aperture ISAR imaging and MTRC correction for maneuvering targets simultaneously. The proposed algorithm achieves better results than traditional algorithms under different signal-to-noise ratio conditions.

Analysis and compensation of stop-and-go approximation in high-resolution spaceborne synthetic aperture radar

ABSTRACT With the advancement of spaceborne synthetic aperture radar (SAR) resolution, the conventional stop-and-go approximation-based motion model is no longer suitable for high-resolution imaging algorithms. This paper conducts a comprehensive analysis of the approximation errors resulting from the stop-and-go approximation and proposes corresponding compensation methods. Firstly, an accurate echo model based on continuous platform motion is derived from the SAR geometry configuration. The impact of these errors on range and azimuth imaging is analysed from a spectral perspective, and the applicable boundary conditions are examined. Subsequently, compensation methods are proposed to eliminate the introduced approximation errors, addressing both”fast time” and”slow time” effects, thus enabling high-resolution imaging through improvements in traditional imaging algorithms. Experimental simulations validate the effectiveness of the error analysis and compensation methods, using X-band low Earth orbit SAR as an illustrative example. The proposed methods are not only compatible with existing mature imaging algorithms, but the analytical approach and compensation methods are also applicable to SAR systems operating on different carrier frequencies, orbits, and platforms.

Detecting delamination defects in CFRP plates using nonlinear defect index of air-coupled Lamb waves and adaptively weighted imaging algorithm

The air-coupled ultrasonic technique is capable of detecting defects in in-service CFRP plates and ensuring their application safety. Traditional air-coupled ultrasonic methods often rely on the linear defect index, which is not effective in characterizing small-size defects. Additionally, the scanning step length completely limits their imaging spatial resolution, resulting in the contradiction between imaging spatial resolution and detection efficiency. To address the above problems, the nonlinear defect index and the adaptively weighted imaging algorithm are proposed. The nonlinear defect index uses the relatively nonlinear coefficient of the Lamb wave to enhance the ability to detect small-size defects. Considering the beam width, the adaptively weighted imaging algorithm constructs the relationship between any imaging point and all scanning paths. At this time, the imaging spatial resolution can be set arbitrarily, eliminating the dependence on the scanning step length. The experimental results demonstrate that the proposed nonlinear defect index can characterize the small-size defect more accurately than the linear defect index. When the scanning step length is increased to improve detection efficiency, the adaptively weighted imaging algorithm can achieve better imaging spatial resolution compared to the traditional defect probability imaging algorithm. For the small-size defect with a diameter of 10 mm, the proposed method has an area detection error of only 7.8 %, whereas the traditional method has an error of 22.4 %. The proposed method is promising to detect small-size defects of in-service CFRP plates in the aircraft skin.

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.

Research on Multi-Person Pose Estimation Technology

Abstract Human pose estimation is a hot topic of computer research in recent years, which promotes the progress of society and brings many conveniences to people’s lives. Fom traditional methods to the mainstream deep learning-based methods, the primary approach in deep learning involves the use of convolutional neural networks to reduce computational complexity and improve network accuracy, but because the network structure is too deep to improve the accuracy, the trained model parameters are also very large, and it is very dependent on the input of hardware equipment. At this time, the lightweight human pose estimation can solve this problem very well. This paper mainly describes the knowledge of convolutional neural network in detail and compares it with traditional image algorithms. The OpenPose model is a classic model based on convolutional neural network that can well achieve single-person and multi-person human pose estimation model, but because the convolution kernel in its network structure is too large to increase the amount of calculation, this paper proposes three improvements to the network structure of the conventional OpenPose model. Finally, the precision of the model is improved by about 40%, which verifies the feasibility of lightweight human body posture estimation research.

Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography Based on ResAttU-Net for Transcranial Brain Hemorrhage Detection.

Hemorrhagic stroke is a leading threat to human's health. The fast-developing microwave-induced thermoacoustic tomography (MITAT) technique holds potential to do brain imaging. However, transcranial brain imaging based on MITAT is still challenging due to the involved huge heterogeneity in speed of sound and acoustic attenuation of human skull. This work aims to address the adverse effect of the acoustic heterogeneity using a deep-learning-based MITAT (DL-MITAT) approach for transcranial brain hemorrhage detection. We establish a new network structure, a residual attention U-Net (ResAttU-Net), for the proposed DL-MITAT technique, which exhibits improved performance as compared to some traditionally used networks. We use simulation method to build training sets and take images obtained by traditional imaging algorithms as the input of the network. We present ex-vivo transcranial brain hemorrhage detection as a proof-of-concept validation. By using an 8.1-mm thick bovine skull and porcine brain tissues to perform ex-vivo experiments, we demonstrate that the trained ResAttU-Net is capable of efficiently eliminating image artifacts and accurately restoring the hemorrhage spot. It is proved that the DL-MITAT method can reliably suppress false positive rate and detect a hemorrhage spot as small as 3 mm. We also study effects of several factors of the DL-MITAT technique to further reveal its robustness and limitations. The proposed ResAttU-Net-based DL-MITAT method is promising for mitigating the acoustic inhomogeneity issue and performing transcranial brain hemorrhage detection. This work provides a novel ResAttU-Net-based DL-MITAT paradigm and paves a compelling route for transcranial brain hemorrhage detection as well as other transcranial brain imaging applications.

A Time Series Forecasting Model Selection Framework using CNN and Data Augmentation for Small Sample Data

The key to the accuracy of time series forecasting is to find the most appropriate forecasting method. Therefore, the forecasting model selection of time series has become a new research hotspot in the data analysis field. However, most of the existing meta learning forecasting model selection methods rely on manual selection of features, which leads to low efficiency and lack of objectivity. Therefore, this paper proposes an improved meta learning framework for deep learning time series forecasting model selection. Inspired by computer vision, we transform one -dimensional time series into two-dimensional images, and use convolution neural network to train and classify time series images (model selection). Moreover, in order to deal with the over fitting problem caused by small sample datasets, the sliding window data augmentation method is used to improve the accuracy of small datasets model selection. The large-scale empirical study on M3 data sets shows that the framework has better model selection accuracy and smaller forecasting error than the recurrent neural network (RNN), back propagation neural network and traditional time series image algorithms. In addition, compared with the traditional time series image method, RNN and BP, the classification rate (model selection accuracy) of this algorithm is improved by 6.5%, 4.4% and 3.2%, respectively.

An optimized deep network based soil texture prediction and vegetation analysis using satellite image database

SummaryThe usage of satellite images has grown rapidly in digital applications. Predicting features in the satellite images using conventional techniques is not an easy task as the images captured from satellites are more complex with highly noisy features. Hence, traditional imaging algorithms cannot analyze the image features and specify the soil textures. In this work, the Tarakeswar satellite images were taken to estimate the soil textures and crops yielding. Hence, to find the soil's chemical properties and suitable crops, the novel bat‐based U‐Net feature prediction system (BUFPS) was developed with the required features. The imported satellite images were initially filtered and entered into the classification phase to forecast the present chemical features and suitable crops in specific soils. After detecting the chemical features, soil textures like sandy, silt, and clay were categorized; the crops like jute, rice, lentil, and potato were considered. Finally, the planned model is executed in the MATLAB environment and has gained outstanding results by achieving the lowest error rate of 8% and a high prediction score of 92%. The proposed model has achieved exact soil texture analysis for complex satellite images. The gained texture analysis outcome is quite better than other existing models.

Detection and Localization of Closely Spaced Pipelines Using a 3-D Multistatic Subsurface SAR

Detection and localization of buried pipelines is an important problem for oil and water distribution industries as well as for excavation and construction companies. A novel 3D forward scattering model and dynamic grid search-based inversion algorithm of multiple buried pipelines using multistatic synthetic aperture radar (SAR) configuration is demonstrated in this article. Traditional imaging algorithms that focus the SAR signals by compensating only for the phase work well when the targets are far away from each other. By considering both the phase and amplitude of the scattered field and casting the inversion problem as an optimization problem, the detection and localization resolutions are improved for extended targets. A forward scattering model for multiple adjacent pipes is developed to track the scattering phase centers of each pipeline as the receiver location is changed. The model is used to calculate the resulting propagation phase and amplitude attenuation due to propagation in lossy media. The misfit between the measured data and the forward model is used as the cost function. The cost function is minimized by the dynamic grid search algorithm that starts with an initial focusing and then performs a more careful search to detect closely spaced pipelines. The forward model and inversion algorithm are validated and compared to a basic focusing algorithm using full-wave simulations as well as experimental data for both metallic as well as dielectric pipes. The results show improvement in range and cross range detection and localization resolutions over the basic SAR focusing algorithm.

Review of development for ptychography algorithm

With the development of synchrotron radiation technology and the improvement of light source coherence, ptychography has developed rapidly. Ptychography algorithm solves the problems of slow convergence and easily falls into the local optimal solution and stagnation of the traditional coherent diffraction imaging algorithm. It has the advantages of large imaging field of view, robustness of algorithm, high tolerance to error and wide range of applications, and is becoming a hot research direction in the field of coherent diffraction imaging. Ptychography reconstructs the complex amplitude distribution and illumination light of the sample by iterative algorithms, which can theoretically reach the resolution of the diffraction limit. It has excellent applications in the fields of wavefront detection, phase imaging and optical metrology. This paper first introduces the background of the proposed ptychography algorithm and briefly describes the problem of coherent diffraction imaging algorithm and its development, and then summarizes the development of ptychography algorithm in detail, mainly including the mainstream algorithm of ptychography and its kernel. This paper then describes in detail the improvement of algorithms corresponding to the improvement of the efficiency of ptychography experiments, correction of position errors and the effect of illumination light multi-modal, and elaborates the algorithm flow. After analyzing the possible intersection of diffraction imaging and neural networks in the field of artificial intelligence, this paper introduces new algorithms with combining ptychography with artificial intelligence. New algorithms with combining ptychography with neural networks will have new potential applications in generality, accuracy and robustness. Finally, a specific parallelization implementation of the ptychography algorithm and common software packages are presented. The logic for writing the parallelization of the algorithm implementation of each package and the corresponding advantages and disadvantages of the packages are described in detail. The characteristics and performance of each package are then listed for reference. This paper helps to establish a global perspective of the algorithm itself, artificial intelligence and computational methods in the field of ptychography, and presents an important reference for systematically developing the ptychography method.

An Improved Imaging Algorithm for HRWS Space-Borne SAR Data Processing Based on CVPRI

In space-borne synthetic aperture radar (SAR), the sliding spotlight mode can acquire images with both high-resolution and wide-swath in azimuth direction. Due to the significant two-dimensional spatial variance of Doppler parameters, the traditional imaging algorithms based on the conventional range models is not available. In this article, the strategy of continuously varying pulse repetition interval (CVPRI) is likely to lead to a novel approach to dealing with the azimuth variance problem to realize high-resolution wide-swath (HRWS) imaging in azimuth direction for sliding spotlight SAR. First, the eighth-order Taylor expansion of the modified equivalent squint range model (MESRM-TE8) is adopted, and the accuracy of MESRM-TE8 is accordingly explained. Then, the properties of the spatial variance for the MESRM-TE8 are analyzed in detail, based on which the strategy of CVPRI is given theoretically to eliminate the azimuth variance. An improved imaging algorithm based on CVPRI is subsequently proposed to address the azimuthal-variant Doppler parameters and realize a batch data processing of a large scene in azimuth frequency domain. The extended scaling method is integrated in this algorithm to uniformly compensate cubic phase modulation introduced by CVPRI and circumvent azimuth time folding caused by subaperture processing in the focused image. Finally, the effectiveness of the CVPRI strategy and the proposed algorithm is demonstrated by the simulation results.

Synthetic aperture imagery for high-resolution imaging sonar

Synthetic aperture sonar (SAS) can provide high-resolution underwater images. Traditional fast imaging algorithms designed for multi-receiver synthetic aperture sonar (MSAS) are complex because the point target reference spectrum (PTRS) deduction and imaging algorithm development are complicated. This paper proposes an imaging algorithm for the MSAS system to solve this issue. The proposed method first approximates the two-round slant range based on the phase center approximation method. The PTRS, including the quasi-monostatic and bistatic deformation terms, can be easily deduced. After compensating for the bistatic deformation term based on the interpolation and complex multiplication with the preprocessing step, the MSAS imagery can be simplified to the focus of the traditional monostatic SAS. Therefore, the conventional imaging algorithms designed for traditional monostatic SAS can be used directly. The proposed method providing high-resolution imaging results is more efficient than the traditional methods.

АНАЛИЗ ПОДСТИЛАЮЩЕЙ ПОВЕРХНОСТИ ПРИ ФОРМИРОВАНИИ ИЗОБРАЖЕНИЯ В РЕЖИМЕ ДОПЛЕРОВСКОГО ОБУЖЕНИЯ ЛУЧА

Radar based on real beam scanning is widely used in both civil and military spheres. However,it is difficult to realize high azimuth resolution of a stationary platform or a platform with nonuniformmotion using conventional signal processing algorithms. Doppler beam sharpening (DBS)technology is a combination of high resolution and real-time performance compared to SyntheticAperture Radar (SAR) technology, which uses the Doppler shift between echoes from objects on theunderlying surface along the azimuth direction, caused by the movement of the radar platform. Unfortunately,the traditional DBS imaging algorithm, which constructs a Doppler filter using an FFT,has a low azimuth resolution and a high level of side lobes, which limits further improvement in azimuthresolution. In the article, the algorithm for constructing a map of the underlying surface in thedirection of movement of the radar carrier based on the DBS was studied and the map image wasanalyzed using the Fourier transform. A three-dimensional view of the map of the underlying surfaceis shown with the distribution of values in the images. The subject of the study is the method and algorithmfor constructing a map of the underlying surface in the Doppler beam sharpening mode andidentifying chain structures based on the analysis of the Fourier transform. The object of the study is a set of test images of the terrain map. The result of the study is the development of an algorithm forconstructing a map in order to identify chain structures on the underlying surface. The novelty of thework is an algorithm that allows you to build a map of the underlying surface based on the DBS,taking into account the blind zone in the direction of movement of the radar carrier. The results obtainedalso make it possible to reveal chain structures in the region of interest. The possibility ofestimating the periodicity of image elements using the Fourier transform has been tested. As a resultof solving the tasks set, the following conclusions can be drawn: – an algorithm has been developedfor constructing a map of the underlying surface based on DBS with image correction in the directionof movement of the radar carrier. – analysis of the results of the study showed that the proposed algorithmallows you to identify chain structures on the map.

A Novel Imaging Scheme of Squint Multichannel SAR: First Result of GF-3 Satellite

The azimuth multichannel synthetic aperture radar (MC-SAR) is an effective means of earth observation due to its high-resolution and wide-swath (HRWS) imaging capability. Compared with the traditional MC-SAR, the two-dimensional (2D) spectrum of the squint multichannel synthetic aperture radar (SMC-SAR) is skewed, leading to the severe coupling between azimuth signals and range signals. Therefore, the traditional imaging algorithms are inapplicable to SMC-SAR. Currently, the existing imaging algorithms for SMC-SAR need interpolation or sub-aperture processing, which results in low efficiency. This paper proposes a novel imaging scheme for spaceborne SMC-SAR to handle this issue. With the squint angle known, the Doppler centroid can be compensated for by the spectrum shift function. Additionally, the frequency correlation method (FCM) is utilized to estimate the channel phase imbalance. After that, the azimuth multichannel data are reconstructed by the filter banks to eliminate the Doppler spectrum aliasing. Finally, the data are imaged based on the modified equivalent slant range model (MESRM). Both the simulation and Gaofen-3 (GF-3) data experiments demonstrate the effectiveness and efficiency of the proposed method.

Defect data image enhancement method based on all-focus imaging algorithm

When the total focus imaging (TFM) algorithm is oriented to multi-layer media, the energy attenuation of the sound beam in the wedge coupling model results in a decrease in detection energy and imaging reliability. In this paper, a new defect data image enhancement method is proposed to calibrate the full-focus imaging model: the attenuation coefficients of different emission-received acoustic beams in the coupled model were measured by the plexiglass reflection method, and the whole matrix captured data were reconstructed. To enhance the image of defect data, multi-threshold fusion is proposed to eliminate the artifacts in the imaging region, especially the noise artifacts in the near field region. Experimental results show that compared with the traditional full-focus imaging algorithm, the self-built algorithm model proposed in this paper greatly improves the energy attenuation problem at the defect with large deflection angle. Especially in the range of excellent detection results, the energy uniformity of the defect is significantly improved, which has a certain inhibitory effect on the artifacts near the defect. The subsequent artifact elimination work improved the defect characterization ability and image signal-to-noise ratio of fully focused images while the effect of near-field artifact elimination was significant.

Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography Based on Sparse Data for Breast Cancer Detection

As a rapidly developing novel electromagnetic imaging technique, microwave-induced thermoacoustic tomography (MITAT) has found many applications and attracted tremendous research interest. Using sparse data to reconstruct images is very challenging for MITAT. This work proposes a novel deep-learning-enabled MITAT (DL-MITAT) modality to address the sparse data reconstruction problem and applies it in breast cancer detection. The applied network is a domain transform network called feature projection network (FPNet) + ResU-Net. Detailed structure and implementation method of the network is described. We conduct both simulation and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex vivo</i> experiments with breast phantoms to test the validity of the DL-MITAT approach. The obtained images given by the trained network exhibit much better quality and have much less artifacts than those obtained by a traditional imaging algorithm. We show that only 15 measurements can still reliably recover an image of the breast tumor for both full-view and limited-view configurations in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex vivo</i> experiments. We also provide detailed discussions on the capability and limitations of the proposed scheme. This work presents a new paradigm for MITAT based on sparse data and can be applied in all related applications of MITAT, including biomedical imaging, nondestructive testing, and therapy guidance.

Image-restoration algorithm based on an underwater polarization imaging visualization model.

The polarization bidirectional reflection distribution function theory of a target is combined with microfacet theory, and the Monte Carlo method is used to establish an underwater laser active-polarization imaging model based on Mie scattering theory. The model presented herein can simulate imaging of an underwater target with a high degree of polarization, and the effects of optical thickness and target surface roughness on active underwater laser imaging results are demonstrated by the simulation image. Combined with histogram equalization and the traditional polarization differential imaging algorithm, an algorithm is presented herein that globally estimates the mutual information value between the target polarization degree and the correction factor of backscattered light polarization degree. The images received from the simulation test can be restored, and results show that the algorithm can restore the target image with a high degree of polarization to some extent. Finally, the correctness of the active underwater laser polarization imaging model and the feasibility of global estimation based on the polarization differential restoration algorithm are verified experimentally.

Multi-Layer Overlapped Subaperture Algorithm for Extremely-High-Squint High-Resolution Wide-Swath SAR Imaging with Continuously Time-Varying Radar Parameters

Extremely-high-squint (EHS) geometry of the traditional constant-parameter synthetic aperture radar (SAR) induces non-orthogonal wavenumber spectrum and hence the distortion of point spread function (PSF) in focused images. The method invented to overcome this problem is referred to as new-concept parameter-adjusting SAR. It corrects the PSF distortion by adjusting radar parameters, such as carrier frequency and chirp rate, based on instant data acquisition geometry. In this case, the characteristic of signal is quite different from the constant-parameter SAR and therefore, the traditional imaging algorithms cannot be directly applied for parameter-adjusting SAR imaging. However, the existing imaging algorithm for EHS parameter-adjusting SAR suffers from insufficient accuracy if a high-resolution wide-swath (HRWS) performance is required. Thus, this paper proposes a multi-layer overlapped subaperture algorithm (ML-OSA) for EHS HRWS parameter-adjusting SAR imaging with three main contributions: First, a more accurate signal model with time-varying radar parameters in high-squint geometry is derived. Second, phase errors are compensated with much higher accuracy by implementing multiple layers of coarse-to-fine spatially variant filters. Third, the analytical swath limit of the ML-OSA is derived by considering both the residual errors of signal model and phase compensations. The presented approach is validated via both the point- and extended-target computer simulations.

Azimuth Interrupted FMCW SAR for High-Resolution Imaging

Azimuth interrupted frequency modulation continuous-wave synthetic aperture radar (AI-FMCW SAR) is a novel type of light and miniaturized sensor that works on satellites, which can transmit and receive signals with a single antenna. Different from conventional pulse SAR, the instantaneous duty cycle of pulse is close to 100%. Several pulses could not be transmitted and would be replaced by receiving echoes. There will be gaps in the received echoes. It is necessary to reconstruct the gapped data; otherwise, there will be ghosting with traditional imaging algorithms. The current algorithms for reconstructing the gapped data are computationally time-consuming and not suitable for high-resolution imaging. Therefore, a new imaging algorithm is proposed to focus on the AI-FMCW SAR raw data via subaperture processing, which is called subaperature sparse reconstructing technique (SSRT). It can significantly suppress ghost targets and has a high calculation speed. In this letter, the spaceborne AI-FMCW SAR signal model was proposed first. Then, SSRT was proposed to process the AI-FMCW SAR data, in which the generalized orthogonal matching pursuit (GOMP) algorithm was used to reconstruct the signals. Finally, the simulation experiments verified the effectiveness and rapidity of the proposed approach.