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The imaging of high-speed high-squint synthetic aperture radar (HSHS-SAR), which is mounted on maneuvering platforms with curved trajectory, is a challenging task due to the existence of 3-D acceleration and the azimuth spatial variability of range migration and Doppler parameters. Although existing imaging algorithms based on linear range walk correction (LRWC) and nonlinear chirp scaling (NCS) can reduce the range–azimuth coupling of the frequency spectrum (FS) and the spatial variability of the Doppler parameter to some extent, they become invalid as the squint angle, speed, and resolution increase. Additionally, most of them ignore the effect of acceleration phase calibration (APC) on NCS, which should not be neglected as resolution increases. For these issues, a modified frequency nonlinear chirp scaling (MFNCS) algorithm is proposed in this paper. The proposed MFNCS algorithm mainly includes the following aspects. First, a more accurate approximation of range model (MAARM) is established to improve the accuracy of the instantaneous slant range history. Second, a preprocessing of the proposed algorithm based on the first range compression, LRWC, and a spatial-invariant APC (SIVAPC) is implemented to eliminate most of the effects of high-squint angle and 3-D acceleration on the FS. Third, a spatial-variant APC (SVAPC) is performed to remove azimuth spatial variability introduced by 3-D acceleration, and the range focusing is accomplished by the bulk range cell migration correction (BRCMC) and extended secondary range compression (ESRC). Fourth, the azimuth-dependent characteristics evaluation based on LRWC, SIVAPC, and SVAPC is completed to derive the MFNCS algorithm with fifth-order chirp scaling function for azimuth compression. Consequently, the final image is focused on the range time and azimuth frequency domain. The experimental simulation results verify the effectiveness of the proposed algorithm. With a curved trajectory, HSHS-SAR imaging is carried out at a 50° geometric squint angle and 500 m × 500 m imaging width. The integrated sidelobe ratio and peak sidelobe ratio of the point targets at the scenario edges approach the theoretical values, and the range-azimuth resolution is 1.5 m × 3.0 m. [ABSTRACT FROM AUTHOR]

Summary: The reconstruction of reflectivity profile of the synthetic aperture radar (SAR) is an important field of research. SAR tomography is an advanced 3D imaging technique for the spectrum estimation in the elevation direction for each azimuth resolution cell. This work presents the processing chain for the tomographic reconstruction from ALOS PALSAR data for an urban region. First, the data are preprocessed by removing the speckle noise followed by atmospheric phase screen and topographic correction. Then the SAR images are stacked together with one master image and the remaining slave images on the baseline value. After the images are coregistered, the interferogram is generated from the image to obtain the difference of the phase value. Then the proposed super resolution SAR (SRS) algorithm is attempted for TomoSAR processing, which combines the functionality of modern machine learning method like deep learning with parametric block‐based compressive sensing approach. Finally, a 3D image is reconstructed from the input data. Evaluation is carried out by comparing the results of the proposed method with other spectrum estimation methods such as nonlinear least square, Capon, and multisignal classification. The normal baseline of the interferometric fringes is about 368.54 m. The proposed SRS algorithm gives improved results with less mean elevation error of 1.8 m and the less standard deviation error of 4.85 m. Finally, the result reveals that the SRS algorithm performed better than other TomoSAR algorithms with the less relative error 0.003. [ABSTRACT FROM AUTHOR]

A novel compressive sensing (CS) synthetic-aperture radar (SAR) called AgileSAR has been proposed to increase swath width for sparse scenes while preserving azimuthal resolution. AgileSAR overcomes the limitation of the Nyquist sampling theorem so that it has a small amount of data and low system complexity. However, traditional CS optimization-based algorithms suffer from manual tuning and pre-definition of optimization parameters, and they generally involve high time and computational complexity for AgileSAR imaging. To address these issues, a pseudo- L 0 -norm fast iterative shrinkage algorithm network (pseudo- L 0 -norm FISTA-net) is proposed for AgileSAR imaging via the deep unfolding network in this paper. Firstly, a pseudo- L 0 -norm regularization model is built by taking an approximately fair penalization rule based on Bayesian estimation. Then, we unfold the operation process of FISTA into a data-driven deep network to solve the pseudo- L 0 -norm regularization model. The network's parameters are automatically learned, and the learned network significantly increases imaging speed, so that it can improve the accuracy and efficiency of AgileSAR imaging. In addition, the nonlinearly sparsifying transform can learn more target details than the traditional sparsifying transform. Finally, the simulated and data experiments demonstrate the superiority and efficiency of the pseudo- L 0 -norm FISTA-net for AgileSAR imaging. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Multistatic airborne SAR (MuA-SAR) benefits from the ability to flexibly adjust the positions of multiple transmitters and receivers in space, which can shorten the synthetic aperture time to achieve the required resolution. To ensure both imaging efficiency and quality of different system spatial configurations and trajectories, the fast factorized back projection (FFBP) algorithm is proposed. However, if the FFBP algorithm based on polar coordinates is directly applied to the MuA-SAR system, the interpolation in the recursive fusion process will bring the problem of redundant calculations and error accumulation, leading to a sharp decrease in imaging efficiency and quality. In this paper, a unified Cartesian fast factorized back projection (UCFFBP) algorithm with a multi-level regional attention strategy is proposed for MuA-SAR fast imaging. First, a global Cartesian coordinate system (GCCS) is established. Through designing the rotation mapping matrix and phase compensation factor, data from different bistatic radar pairs can be processed coherently and efficiently. In addition, a multi-level regional attention strategy based on maximally stable extremal regions (MSER) is proposed. In the recursive fusion process, only the suspected target regions are paid more attention and segmented for coherent fusion at each fusion level, which further improves efficiency. The proposed UCFFBP algorithm ensures both the quality and efficiency of MuA-SAR imaging. Simulation experiments verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Recently, synthetic aperture radar (SAR) target detection algorithms based on Convolutional Neural Networks (CNN) have received increasing attention. However, the large amount of computation required burdens the real-time detection of SAR ship targets on resource-limited and power-constrained satellite-based platforms. In this paper, we propose a hardware-aware model speed-up method for single-stage SAR ship targets detection tasks, oriented towards the most widely used hardware for neural network computing—Graphic Processing Unit (GPU). We first analyze the process by which the task of detection is executed on GPUs and propose two strategies according to this process. Firstly, in order to speed up the execution of the model on a GPU, we propose SAR-aware model quantification to allow the original model to be stored and computed in a low-precision format. Next, to ensure the loss of accuracy is negligible after the acceleration and compression process, precision-aware scheduling is used to filter out layers that are not suitable for quantification and store and execute them in a high-precision mode. Trained on the dataset HRSID, the effectiveness of this model speed-up algorithm was demonstrated by compressing four different sizes of models (yolov5n, yolov5s, yolov5m, yolov5l). The experimental results show that the detection speeds of yolov5n, yolov5s, yolov5m, and yolov5l can reach 234.7785 fps, 212.8341 fps, 165.6523 fps, and 139.8758 fps on the NVIDIA AGX Xavier development board with negligible loss of accuracy, which is 1.230 times, 1.469 times, 1.955 times, and 2.448 times faster than the original before the use of this method, respectively. [ABSTRACT FROM AUTHOR]

The importance of monitoring sea level in coastal zones becomes more and more obvious in the era of global climate change, because, in coastal zones, although satellite altimetry is an ideal tool in measuring sea level over open ocean, but its accuracy often decreases significantly at coast due to land contamination. Although the accuracy of waveform processing algorithms for synthetic aperture altimeters has been improved in the last decade, the computational speed is still not fast enough to meet the requirements of real-time processing, and the accuracy cannot meet the needs of nearshore areas within 1 km from the coast. To improve the efficiency and accuracy in the coastal zone, this study proposed an innovative waveform retracking scheme for the coastal zone based on a multiple optimization parabolic cylinder algorithm (MOPCA) integrated with machine learning algorithms such as recurrent neural network and Bayesian estimation. The algorithm was validated using 153-pass repeat cycle data from Sentinel-6 over Qianliyan Island and Hong Kong–Wanshan Archipelago. The computational speed of the proposed algorithm was four to five times faster than the current operational synthetic aperture radar (SAR) retracking algorithm, and its accuracy within 0–20 km from the island was comparable to the most popular SAMOSA+ algorithm, better than the official data product provided by Sentinel-6. Especially, the proposed algorithm demonstrates remarkable stability in the sense of proceeding speed. It maintains consistent performance, even when dealing with intricate wave patterns within a proximity of 1 km from the coast. The results showed that the proposed scheme greatly improved the quality of coastal altimetry waveform retracking. [ABSTRACT FROM AUTHOR]

As a new algorithm to achieve sparse aperture inverse synthetic aperture radar (SA‐ISAR) imaging, the alternating direction method of multipliers (ADMM) suffers from selection of model parameters and the inability to discriminate the target and non‐target components. Therefore, an improved ADMM (I‐ADMM) algorithm is proposed to solve these problems. First, the particle swarm optimisation (PSO) algorithm is utilised to obtain the optimal model parameters. Aiming to adequately preserve the target's shape information in the ISAR image, a modified image entropy (IE) is proposed to be the objective function of the PSO. Then, a new regularisation function is obtained from the ADMM based ISAR image. The proposed algorithm can enhance the target components and suppress the non‐target components by utilising the regularisation function. Finally, results based on the simulated and measured data show that the proposed I‐ADMM algorithm not only achieves at least 9.4% improvement in IE but also greatly improves the image quality as visually compared with the traditional ADMM algorithm which validates the effectiveness and superiority of the proposed algorithm. [Correction added on 13/09/23, after first online publication: In the following sentence in abstract "Finally, results based on... the proposed algorithm." 10% was changed as 9.4%.] [ABSTRACT FROM AUTHOR]

In existing imaging algorithms for wide-beam multiple-receiver synthetic aperture sonar (SAS) systems, the double-square-root (DSR) range history of each receiver is generally converted into the sum of a single-square-root (SSR) range history and an error term using displaced phase center aperture (DPCA) approximation. Therefore, before imaging, each receiver's error term needs to be individually compensated in the azimuth frequency domain, which is computationally expensive. As a result, a novel wide-beam multiple-receiver SAS system algorithm with low complexity and high precision is suggested. First, the translation relationship between the range histories of the reference receiver and other receivers is used to derive an SSR approximation range history that takes into account the azimuth variance of the non-stop-hop-stop time while ignoring differential range curvature (DRC) between the range histories from different receivers. Then, using the principle of stationary phase (POSP), the two-dimensional (2-D) spectrum of the point target is obtained. Finally, the multiple-receiver data are transformed into monostatic SAS-equivalent data for imaging after phase correction, time delay correction, and azimuth reconstruction. The range-Doppler (RD) algorithm is used as an example to explain the specific steps of the proposed approach. Simulation data and ChinSAS data experiments verify that the proposed algorithm achieves an imaging performance that is comparable to that of the existing wide-beam algorithm, but with much higher computational efficiency, making it suitable for real-time imaging. [ABSTRACT FROM AUTHOR]

Multiple-input-multiple-output synthetic aperture radar (MIMO-SAR) is being studied and applied in more and more scenarios. However, there is still a certain distance away from real-time imaging using advanced algorithms. The traditional backpropagation algorithm (BPA) multi-accumulation integration is unsuitable for dealing with large-size scanning data, and the wavenumber domain algorithm requires the array to satisfy Nyquist sampling law in azimuth to avoid aliasing in imaging reconstruction. Based on these issues, a novel 3D imaging method is proposed for MIMO-SAR. An appropriate transformation and inverse Fourier transform (FT) is carried out for the frequency domain; thus, accumulation in the wavenumber domain is not required, which is easy to implement. The computational complexity of the algorithm is much lower than BPA and has better generalizability than the wavenumber domain algorithm. Coherence factor (CF) is also introduced to achieve sidelobe suppression. Proof-of-principle experiments were also carried out in the 92.5 GHz band based on the MIMO-SAR prototype system. Both simulation and experimental results of different distributed targets show good performance of imaging and do not lose the quality of image reconstruction. [ABSTRACT FROM AUTHOR]

Terahertz video synthetic aperture radar (THz-ViSAR) has tremendous research and application value due to its high resolution and high frame rate imaging benefits. However, it requires more efficient imaging algorithms. Thus, a novel multistage back projection fast imaging algorithm for the THz-ViSAR system is proposed in this paper to enable continuous playback of images like video. The radar echo data of the entire aperture is first divided into multiple sub-apertures, as with the fast-factorized back projection algorithm (FFBP). However, there are two improvements in sub-aperture imaging. On the one hand, the back projection algorithm (BPA) is replaced by the polar format algorithm (PFA) to improve the sub-aperture imaging efficiency. The imaging process, on the other hand, uses the global Cartesian coordinate system rather than the local polar coordinate system, and the wavenumber domain data of the full aperture are obtained step by step through simple splicing and fusion, avoiding the amount of two-dimensional (2D) interpolation operations required for local polar coordinate system transformation in FFBP. Finally, 2D interpolation for full-resolution images is carried out to image the ground object targets in the same coordinate system due to the geometric distortion caused by linear phase error (LPE) and the mismatch of coordinate systems in different imaging frames. The simulation experiments of point targets and surface targets both verify the effectiveness and superiority of the proposed algorithm. Under the same conditions, the running time of the proposed algorithm is only about 6% of FFBP, while the imaging quality is guaranteed. [ABSTRACT FROM AUTHOR]

Aiming at improving the performance of scale invariant feature transform (SIFT) algorithm during the registration of optical and synthetic aperture radar (SAR) images, a new SIFT algorithm is proposed. Firstly, the nonlinear diffusion scale space of optical and SAR images is constructed by using nonlinear diffusion filtering, the uniform gradient information is calculated by using multi-scale Sobel operator and multi-scale exponential weighted mean ratio operator respectively. Then, after removing the first layer of the scale space with the image blocking strategy, the scale space is partitioned, and Harris feature points are extracted on the basis of consistent gradient information to obtain stable and uniform point features. Descriptors are constructed based on gradient position and direction histogram templates and normalized to overcome nonlinear radiation differences between images. Finally, the correct matching point pairs are obtained by using bilateral fast approximate nearest neighbor (FLANN) search matching method and random sampling consistent (RANSAC) method, and the affine transformation model parameters are obtained. Compared with the other two algorithms, the CMR of this algorithm is improved by 80.53%, 75.61% and 81.74% respectively in the three groups of images, and the RMSE is reduced by 0.6491, 1.0287 and 0.6306 respectively. [ABSTRACT FROM AUTHOR]

In the global navigation satellite system-based synthetic aperture radar interferometry (GNSS-based InSAR) system, 3D deformation retrieval needs to associate multiangle images, but due to the low 2D resolution, traditional texture-based algorithm cannot be used in GNSS-based system. At the same time the association error introduced by the elevation error also cannot be compensated for. In this paper, a multiangle images aggregation analysis and association algorithm is proposed under elevation error conditions. Instead of using texture information, focus positions from different angles are adopted here. First, the focus position with elevation error is modelled in the bistatic configuration. Then, the images association is achieved based on the aggregation analysis, which is further quantitatively expressed through minimum circle principle. Raw data from four satellites are used to demonstrate the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Shadow detection is a new method for video synthetic aperture radar moving target indication (ViSAR-GMTI). The shadow formed by the target occlusion will reflect its real position, preventing the defocusing or offset of the moving target from making it difficult to identify the target during imaging. To achieve high-precision shadow detection, this paper proposes a video SAR moving target shadow-detection algorithm based on low-rank sparse decomposition combined with trajectory area extraction. Based on the low-rank sparse decomposition (LRSD) model, the algorithm creates a new decomposition framework combined with total variation (TV) regularization and coherence suppression items to improve the decomposition effect, and a global constraint is constructed to suppress interference using feature operators. In addition, it cooperates with the double threshold trajectory segmentation and error trajectory elimination method to further improve the detection performance. Finally, an experiment was carried out based on the video SAR data released by Sandia National Laboratory (SNL); the results prove the effectiveness of the proposed method, and the detection performance of the method is proved by comparative experiments. [ABSTRACT FROM AUTHOR]

The back-projection algorithm can adapt to synthetic aperture radar imaging in any mode because of its time-domain imaging characteristics, so it has received increasing attention in scientific research and engineering practice. However, the large enormous of computation of the back-projection algorithm limits its application. Since the back-projection algorithm has a high degree of data parallelism, it is very suitable for GPU parallel processing. The data scale and hardware environment in different application scenarios will result in different bottlenecks which may prevent the back-projection algorithm from performing optimally. Thus, this paper proposes a series of strategies to aid the back-projection algorithm in achieving the best peak performance with any given problem scale on any given platform. A heuristic block strategy is proposed to optimize the peak performance of the back-projection algorithm on servers and miniaturized GPU devices, which can handle the differences in hardware platforms as well as the differences in data scales. To optimize the peak performance of the back-projection algorithm on miniaturized GPU devices, a memory management strategy is proposed by using unified memory and pinned host memory. The method proposed in this paper has achieved an acceleration ratio close to the device's peak performance on servers and miniaturized GPU devices. By using the proposed methods, any algorithm with data-independent chunking and any GPU device with a uniform memory architecture can achieve higher peak performance. [ABSTRACT FROM AUTHOR]

This letter proposes an improved synthetic aperture imaging algorithm to enhance the imaging performance in the Azimuth Missing Data (AMD) case. The proposed algorithm designs an effective phase compensation function based on the Range Cell Migration Correction (RCMC) echo and first reconstructs the complete RCMC echo from the AMD echo. The accuracy of echo reconstruction is improved, leading to a better imaging result. Numerical simulations and a real measured experiment verify the effectiveness of the proposed algorithm. Furthermore, since the range positions of targets can be located after RCMC processing, only non‐zero RCMC signals are reconstructed. Thus, the computational complexity superiority of the proposed algorithm is obvious, especially when the targets are sufficiently sparse in the range direction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years, bistatic inverse synthetic aperture radar (Bi‐ISAR) imaging of targets with sparse aperture has attracted more and more attention. One of the critical issues is that the Bi‐ISAR imaging of some large targets with rotational motions is prone to migration through resolution cells, which increases the imaging difficulty. In order to enhance the compensation for through resolution cell migration in those scenarios and improve the accuracy and resolution of Bi‐ISAR images under sparse aperture, that is, improving the target recognition of Bi‐ISAR, the authors aimed at the high‐order migration term and the high‐order phase error term and proposed a Bi‐ISAR sparse aperture high‐resolution imaging algorithm based on complex Bayesian compressed sensing and frequency resampling. The algorithm was solved by 'distributed' iteration. The migration compensation under the sparse aperture conditions was completed, and the quality of the reconstructed images was improved. Simulation results verified the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Radar forward‐looking imaging is gaining significance in various applications like battlefield reconnaissance, target surveillance, and precision guidance. Although synthetic aperture radar techniques provide high azimuth resolution but faced limitations in forward‐looking area due to the poor Doppler resolution and the "left‐right" ambiguity problem. Recently, generative adversarial networks have been extensively used for image motion blur removal. This letter proposes an end‐to‐end forward‐looking image enhancing network using generative adversarial network to produce high‐resolution images, improving the efficiency, and quality of imaging. Compared to conventional methods such as the deconvolution‐based methods, this algorithm eliminates the need for design and iterative processes of the observation matrix. Simulated and real radar data validate that this approach offers robust recovery and better performance. [ABSTRACT FROM AUTHOR]

In this Letter, the authors present a novel imaging algorithm for multireceiver synthetic aperture sonar (SAS). The Loffeld's bistatic formula (LBF) including quasi‐monostatic (QM) and multireceiver deformation (MD) phases is first reformulated into range‐variant and range‐invariant phases. After compensating the range‐invariant phase, the subsequent steps are the extended Stolt mapping for each bistatic SAS. Based on simulations, the presented method can also provide high‐resolution result which is similar to that of back projection (BP) algorithm. [ABSTRACT FROM AUTHOR]

In our experiments, we develop and test portable multi-element receiver antenna arrays, electrically scanned in order to immediately obtain a recognizable image of subsurface objects. Two quadrature components of the radar return signal are processed with a Kirchhoff backward migration algorithm. Physical theory is used to assess the quality of the holographic image, and the synthetic aperture approach is developed and tested. The parabolic wave equation and Gaussian beam technique are used in order to take into account refraction effects and to suppress specular reflection from the air-ground interface. Laboratory and field tests confirmed the predicted device parameters. [ABSTRACT FROM AUTHOR]

Superpixel segmentation for polarimetric synthetic aperture radar (PolSAR) images plays a key role in remote-sensing tasks, such as ship detection and land-cover classification. However, the existing methods cannot directly generate multi-scale superpixels in a hierarchical style and they will take a long time when multi-scale segmentation is executed separately. In this article, we propose an effective and accurate hierarchical superpixel segmentation method, by introducing a minimum spanning tree (MST) algorithm called the Boruvka algorithm. To accurately measure the difference between neighboring pixels, we obtain the scattering mechanism information derived from the model-based refined 5-component decomposition (RFCD) and construct a comprehensive dissimilarity measure. In addition, the edge strength map and homogeneity measurement are considered to make use of the structural and spatial distribution information in the PolSAR image. On this basis, we can generate superpixels using the distance metric along with the MST framework. The proposed method can maintain good segmentation accuracy at multiple scales, and it generates superpixels in real time. According to the experimental results on the ESAR and AIRSAR datasets, our method is faster than the current state-of-the-art algorithms and preserves somewhat more image details in different segmentation scales. [ABSTRACT FROM AUTHOR]

The need to successfully navigate in the absence of GNSS has grown in recent years. In particular, light aircraft such as UAVs are growing in popularity for a variety of applications vulnerable to GPS denial. The research presented here develops a GPS-denied navigation scheme for light aircraft employing images formed from a synthetic aperture radar system. Past research has explored the utility of radar telemetry in GPS-denied systems. This research advances previous work by exploiting radar images to obtain range and cross-range position measurements. Images are formed using the Range-Doppler Algorithm, an efficient image formation algorithm ideal for the sometimes limited processing packages available to light aircraft. An inertial navigation and radar processing system is implemented using both real and simulated radar images to aid in estimating an aircraft’s state in a GPS-denied environment. The results show that navigation in the absence of GPS using synthetic aperture radar is feasible with converging and bounded estimation errors. [ABSTRACT FROM AUTHOR]

Light field (LF) image depth estimation is a critical technique for LF-related applications such as 3D reconstruction, target detection, and tracking. The refocusing property of LF images provide rich information for depth estimations; however, it is still challenging in cases of occlusion regions, edge regions, noise interference, etc. The epipolar plane image (EPI) of LF can effectively deal with the depth estimation because of its characteristics of multidirectionality and pixel consistency—in which the LF depth estimations are converted to calculate the EPI slope. This paper proposed an EPI LF depth estimation algorithm based on a directional relationship model and attention mechanism. Unlike the subaperture LF depth estimation method, the proposed method takes EPIs as input images. Specifically, a directional relationship model was used to extract direction features of the horizontal and vertical EPIs, respectively. Then, a multiviewpoint attention mechanism combining channel attention and spatial attention is used to give more weight to the EPI slope information. Subsequently, multiple residual modules are used to eliminate the redundant features that interfere with the EPI slope information—in which a small stride convolution operation is used to avoid losing key EPI slope information. The experimental results revealed that the proposed algorithm outperformed the compared algorithms in terms of accuracy. [ABSTRACT FROM AUTHOR]

When synthetic aperture radar (SAR) images are denoised by non‐local mean (NLM) algorithm, logarithmic transformation will lead to the loss of some image information. To keep the details and smooth the noise of the SAR images better, a new sub‐region NLM denoising algorithm with the statistical characteristics of SAR image is proposed in this paper. Firstly, the probability distribution image is generated by calculating the probability value of every pixel. Then the images can be divided into the heterogeneous region and the homogeneous region by the threshold obtained with the variation coefficient of the probability image. A new filtering weight using both the original and probability images is generated based on NLM in the heterogeneous region. The filtering weight is obtained using the probability image in the homogeneous region. This method fully considers the characteristics of noise in different regions. Multi‐SAR image experiments demonstrate the advantages of noise smooth and detail protection. [ABSTRACT FROM AUTHOR]

Emitter localization is an active research topic in electronic reconnaissance. Recently, passive synthetic aperture (PSA) has been used to determine the azimuth and range distance of the emitter. However, the existing methods approximate the distance equation of the spaceborne model through the airborne model, resulting in a large positioning error. In this article, an emitter localization algorithm based on PSA in the spaceborne model is proposed. First, the phase of the received signal is accumulated by using the symmetry of the Doppler history of the received signal in long synthetic aperture time, improving the azimuth positioning accuracy. The effective velocity is then used to establish the nonlinear equations of range distance. A modulus function is used to solve the equations, correcting the range positioning error in the spaceborne model. Finally, the influence of noise, satellite position error, and satellite speed error on the positioning results is analyzed. The error matrix of the proposed method in wide swath is obtained. Simulation results show that the positioning accuracy of this method is one order of magnitude higher than that of traditional single-satellite positioning methods. The effectiveness of the proposed method is verified through actual satellite experiments. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) data provide an alternative way for topography retrieval, especially when limited PolSAR data are available. This article proposes a novel topography retrieval algorithm based on the Lambertian backscatter model that further improves the vertical precision of digital elevation model (DEM) generation and requires only one flight. The key idea of the proposed algorithm is to avoid data fluctuations caused by the ratio of the azimuth slope angle to the polarimetric orientation angle (POA). The previous research has confirmed the feasibility of generating a DEM based on single-pass PolSAR data, but its effect on the quality of reference DEM has not been well-explained. To analyze this effect, a large number of experiments on DEM with different resolutions are conducted. In addition, an in-depth analysis of non-linear and terrain-dependent errors is performed. The L-band PolSAR data of NASA/JPL TOPSAR and ALOS-2 PALSAR-2 and interferometric SAR (InSAR) DEM data are used to verify the proposed algorithm. The experimental results show that PolSAR data can be used as an additional reliable information source for DEM fusion under certain conditions to improve the quality of public DEM. [ABSTRACT FROM AUTHOR]

For the near-field synthetic aperture interferometric radiometer (SAIR), the Fourier transform relationship between the visibility function and the near-field brightness temperature (BT) distribution is not valid. It is a challenging task for near-field SAIR imaging to realize very close-range accurate imaging with a large field of view (FOV). This article presents a new SAIR near-field imaging algorithm based on the angular spectrum theory to realize the passive millimeter-wave (PMMW) imaging, called synthetic-angular-spectrum imaging (SASI) algorithm. This SASI algorithm mainly addresses data samples of a 4-D visibility function acquired from planar arrays. First, we invert 4-D visibility samples into the angular spectrum domain via the Fourier transformation. The obtained angular spectrum data cannot be directly used to estimate the near-field BT distribution. Second, a dedicated phase factor compensation is adopted for handling this problem. The dimension-reducing accumulation is employed to generate the synthetic angular spectrum (SAS) of near-field BT distribution. Finally, we reconstruct the BT image by using the generated SAS data. Simulation and experiment results show that the presented SASI algorithm has the superiority on image reconstruction quality, especially for the off-axis regions, compared with the existing near-field SAIR imaging methods. [ABSTRACT FROM AUTHOR]

Unsupervised methods play an essential role in polarimetric synthetic aperture radar (PolSAR) image classification, where labeled data are difficult to obtain. However, there is still a large gap between existing unsupervised learning methods and supervised learning methods. Without the semantic constraints of labeled data, pixels within the same category are often misclassified into different categories, leaving the output to be messy. To address the previous issue, this article proposes a fully unsupervised pipeline for training convolutional neural networks (CNNs). The pipeline combines low-level superpixels and high-level CNN semantic features for high-quality pseudolabel generation. It effectively eliminates the misclassified pixels by voting within the superpixel blob while preserving the sharpness of edges. With the training process of the model, the quality of the generated labels is getting improved. Experiments on airborne [experimental airborne SAR system from Germany (ESAR)/airborne synthetic aperture radar from America (AIRSAR)] and spaceborne (RadarSat2) PolSAR images prove the effectiveness of the proposed method (measured with overall accuracy (OA), average accuracy (AA), and Kappa metrics). Our method outperforms the previous unsupervised methods (H/alpha-Wishart, SM-Wishart, FDD-H, DEC, and VQC-CAE) with a large margin and even has comparable performance to the supervised CNN model [fully CNN (FCN)]. [ABSTRACT FROM AUTHOR]

Synthetic aperture sonar (SAS) systems produce high-resolution images of the seabed environment. Moreover, deep learning has demonstrated superior ability in finding robust features for automating imagery analysis. However, the success of deep learning is conditioned on having lots of labeled training data but obtaining generous pixel-level annotations of SAS imagery is often practically infeasible. This challenge has thus far limited the adoption of deep learning methods for SAS segmentation. Algorithms exist to segment SAS imagery in an unsupervised manner, but they lack the benefit of state-of-the-art learning methods and the results present significant room for improvement. In view of the above, we propose a new iterative algorithm for unsupervised SAS image segmentation combining superpixel formation, deep learning, and traditional clustering methods. We call our method iterative deep unsupervised segmentation (IDUS). IDUS is an unsupervised learning framework that can be divided into four main steps: 1) a deep network estimates class assignments; 2) low-level image features from the deep network are clustered into superpixels; 3) superpixels are clustered into class assignments (which we call pseudo-labels) using $k$ -means; and 4) resulting pseudo-labels are used for loss backpropagation of the deep network prediction. These four steps are performed iteratively until convergence. A comparison of IDUS to current state-of-the-art methods on a realistic benchmark dataset for SAS image segmentation demonstrates the benefits of our proposal even as the IDUS incurs a much lower computational burden during inference (actual labeling of a test image). Because our design combines merits of classical superpixel methods with deep learning, practically we demonstrate a very significant benefit in terms of reduced selection bias, i.e., IDUS shows markedly improved robustness against the choice of training images. Finally, we also develop a semi-supervised (SS) extension of IDUS called Iterative Deep SS Segmentation (IDSS) and demonstrate experimentally that it can further enhance performance while outperforming supervised alternatives that exploit the same labeled training imagery. [ABSTRACT FROM AUTHOR]

Arc-synthetic aperture radar (ArcSAR) can observe the surrounding ground objects at 360°, with the advantages of a wide imaging range and constant azimuth angular resolution. In this article, based on the stepped frequency continuous wave (SFCW), fourth-order Taylor expansion, and the series-inversion method, the accurate phase expression of the 2-D spectrum is derived, and then, the 2-D frequency-domain imaging algorithm (2-D-FDA) for ArcSAR is realized. In the simulation part, the imaging results of 2-D-FDA are compared with the backprojection algorithm (BPA) and Liao’s method, which is the reference for 2-D-FDA. The simulation results show that the 2-D-FDA is more applicable to process the data obtained from the antenna with the beamwidth larger than 70°. The SFCW-ArcSAR system consists of a vector network analyzer (VNA), a rotating platform, two antennas, and a control computer. Then, the system is used for the outdoor experiment. The experimental results of the corner reflector show that the point target analysis of 2-D-FDA is consistent with those of the BPA and theory. Under the system parameters in this article, the measured azimuth angular resolution is 0.0177 radian, the peak sidelobe ratio (PSLR) in the range direction is −12.46 dB, and the range resolution is 0.48 m on the ground-range plane. The imaging results of the real scenario processed by BPA and 2-D-FDA are consistent, and the ArcSAR image agrees with the optical image of the experimental scene. The effectiveness and correctness of the 2-D-FDA are verified by simulation and experiment. [ABSTRACT FROM AUTHOR]

As a wideband radar system, a synthetic aperture radar (SAR) may conflict with several electromagnetic systems, such as frequency modulation (FM), TV, and other communication systems. These signals, termed as radio frequency interference (RFI), may severely interfere SAR systems from generating a high-resolution image. Numerous previous researches focused on the RFI suppression problem, among which the semiparametric methods have been verified to have the state-of-the-art performance. However, most of the semiparametric methods are computationally expensive and can hardly be used on wide-swath SAR imaging processing. In this article, an efficient semiparametric algorithm is proposed to suppress RFIs via alternating projections. It has comparable performance as the other methods but significantly improves the computational efficiency a lot. It is able to remove both narrowband and wideband RFIs and can be used directly on the Level-1 SAR data. Finally, multiple real SAR data are provided to demonstrate the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Radar imaging using the synthetic aperture radar (SAR) principle is a common method to obtain information about e.g., the surface of a target. However, most image formation algorithms for such systems assume (quasi-)static measurements. This may lead to errors in the processed images if the sensor is moving during the measurement process. This is especially the case for frequency-modulated continuous-wave (FMCW)-based sensors since the signal duration is longer than in a pulsed system and the achievable bandwidth is much larger and introduces additional challenges. Motion compensation in the context of radar imaging is usually related to the correction of deviations from an ideal trajectory. In contrast, this article presents a method to take the sensor movement during a single FMCW ramp into account and therefore addresses the effects caused by a continuous path during the transmit/receive process. Hence, faster movement can be achieved during the scanning of the synthetic aperture without being bound by stop-and-go approximations. In addition, it will be shown that the algorithm is suitable to reduce systematic errors due to aliasing caused by spatial sampling below the Nyquist rate. For this purpose, this article presents simulations and measurement results, obtained by an ultrawideband $D$ -band FMCW radar operating between 122 and 170 GHz. [ABSTRACT FROM AUTHOR]

In the linear array synthetic aperture radar (LASAR) three-dimensional (3D) imaging, the spacing between adjacent elements in the uniform linear array (ULA) must satisfy the Nyquist sampling theorem to avoid the grating lobes, which makes the number of elements in the ULA very large. To reduce the elements in the ULA, the coprime adjacent array (CAA) with the same aperture length as the ULA is used when conducting LASAR 3D sparse imaging by compressed sensing (CS) algorithms. However, due to the increased autocorrelation coefficient of the measurement matrix, there exists grating lobes interference in the CAA-SAR imaging results. To solve this problem, we propose a joint sparse recovery (JSR) algorithm for CAA-SAR 3D sparse imaging. Firstly, we conduct sparse imaging on the CAA and its two subarrays, respectively. Secondly, the imaging results of the CAA and its two subarrays are performed image segmentation by the OTSU algorithm to extract their target-areas' imaging results. Finally, we perform the image fusion by the wavelet transform on the target-areas' imaging results to obtain the final imaging results. Both simulation and experimental results indicate that the imaging quality and computational efficiency of the JSR algorithm are higher than the random sampling array (RSA) and CAA under the same number of array elements. Besides, under the same aperture length, the JSR algorithm improves the computational efficiency than the ULA without imaging-quality loss. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar systems are capable of acquiring high-resolution images by modulating and transmitting waves in high frequency levels towards the ground. Synthetic Aperture Radar (SAR) images are generated by demodulating and processing the backscattered waves by the targets. New SAR systems produce high-resolution images by raising the modulation frequency. The sampling time becomes shorter and a large number of samples are generated, which costs for processing and storage. Compressed Sensing (CS) is a compression technique capable of reconstructing a sparse signal from a small number of samples inferior to the conventional sampling. The representation basis, that projects a signal to a sparse form is the key of the application of CS. CS becomes unusable in the acquisition of heterogeneous scenes that present many scatterings types because the representation matrices are unknown and require intensive computing to generate them. In the literature, most proposed researches in the CS field apply CS on simulated sparse scenes, where only a few number of strong scatters are present to avoid the representation matrix computing. The purpose of this paper is to apply CS on non-sparse images and to avoid heavy computation to generate a representation matrix at the same time by combining the CS with the Gaussian Process regression (GPR). A small number of backscattered signals are acquired and sampled with respect to the Nyquist theory, sparsified and used as a feature vector for the training of the GPR model. The remaining signals are processed by the CS theory. After reconstruction, the zero values of the image are predicted using the model generated by the GPR algorithm. Five strategies are proposed in this paper and several evaluations are performed. These methods reconstruct an exploitable image from 40 % of samples. [ABSTRACT FROM AUTHOR]

Laser ultrasonic synthetic aperture focusing technique (SAFT) is susceptible to multiple modes of ultrasonic generated by laser pulse. In this work, we propose an optimized synthetic aperture imaging method based on the differential technique, resulting in the effective detection of internal defects. The laser ultrasonic SAFT finite element model is established, and the models are calculated in the case of existing internal defect or not. Thereby, the original ultrasonic data containing the defect reflected waves and the differential data are obtained, and the internal defects are reconstructed by the delay superposition algorithm. The results show that the defect imaging is submerged in the high-amplitude background noise superimposed by the surface acoustic wave; when only the original data are used. However, the optimized SAFT imaging based on the differential technique can not only reduce the interference of other mode waves on the imaging area significantly, but also retain the defect reflected waves effectively. Moreover, the imaging and precise locating of multiple defects are realized, which pave the way for enhancing the internal defects detection ability of laser ultrasonic. [ABSTRACT FROM AUTHOR]

We have developed C-band compact polarimetry geophysical model functions for RADARSAT Constellation Mission ocean surface wind speed retrieval. A total of 1594 RADARSAT-2 images acquired in quad-polarization SAR imaging mode were collocated with in situ buoy observations. This dataset is first used to simulate compact polarimetric data and to examine their dependencies on radar incidence angle and wind vectors. We find that right circular transmit, right circular receive (RR-pol) radar backscatters are less sensitive to incidence angles and wind directions but are more dependent on wind speeds, compared to right circular transmit, horizontal receive (RH-pol), right circular transmit, vertical receive (RV-pol), and right circular transmit, left circular receive (RL-pol). Subsequently, the matchup data pairs are used to derive the coefficients of the transfer functions for the proposed compact polarimetric geophysical model (CMOD) functions, and to validate the associated wind speed retrieval accuracy. Statistical comparisons show that the retrieved wind speeds from CMODRH, CMODRV, CMODRL, and CMODRR are in good agreement with buoy measurements, with root-mean-square errors of 1.38, 1.51, 1.47, and 1.25 m s−1, respectively. The results suggest that compact polarimetry is a good alternative to linear polarization for wind speed retrieval. CMODRR is more appropriate to retrieve high wind speeds than CMODRH, CMODRV or CMODRL. [ABSTRACT FROM AUTHOR]

The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features. [ABSTRACT FROM AUTHOR]

In synthetic aperture sonar (SAS) image, the underwater shipwreck targets are often buried by sediment or badly damaged. Only a part of the characteristics of artificial objects is retained. In this paper, firstly, based on the analysis of the ocean buried background, the Meanshift filtering is used to smooth the original image and convert the color image into binary one. Secondly, the residual contour of artificial target is extracted through the modified Canny edge detection algorithm. Thirdly, the Region Growing method is taken to remove the discrete interference and keep the intact edge of the line. Consideration with the principle of line alignment, the contours of shipwreck targets are gradually connected and aggregated. Finally, a large amount of measured practical SAS images are tested. The experimental results verified that the proposed algorithm can accurately detect the shipwreck target based on residual contour information, meanwhile with an acceptable timeliness for large size sonar image data. [ABSTRACT FROM AUTHOR]

In order to reduce the multiplicative speckle noise of synthetic aperture radar SAR image. an improved non-local mean (NLM) algorithm is proposed in this paper. Firstly, the coefficient of variation (CV) was used to estimate the SAR image region division threshold and estimate area division factor based on threshold. Thus. the SAR image was divided into texture area and homogeneous area by area division factor. Then. similarity windows of different scales were used to estimate the ESM corresponding to pixels in different regions, and the ESM matrix of each pixel in the search window under the similarity window and the ESM matrix of the center pixel were selected further. Moreover. the two-norm of the ratio of the two corresponding elements were used to construct the similarity measurement parameter that adapts to the multiplicative noise. filter value was obtained by combining the similarity parameter with the adaptive de cay factor dominated by CV. Finally, the non local average weight filtering of the SAR image was realized by different regions with different filtering values. Compared with NLM filtering algorithm based on the mean ratio and the coefficient of variation. the proposed algorithm performs better in that the equivalent numbers of looks increased by more than 0,3% and edge preservation index increased by more than 0.1% [ABSTRACT FROM AUTHOR]

In this paper two simple synthetic aperture radar (SAR) methods are applied on data from a 24 GHz FMCW radar implemented on a linear drive for educational purposes. The data of near and far range measurements are evaluated using two different SAR signal processing algorithms featuring 2D-FFT and frequency back projection (FBP) method. A comparison of these two algorithms is performed concerning runtime, image pixel size, azimuth and range resolution. The far range measurements are executed in a range of 60 to 135 m by monitoring cars in a parking lot. The near range measurement from 0 to 5 m are realised in a measuring chamber equipped with absorber foam and nearly ideal targets like corner reflectors. The comparison of 2D-FFT and FBP algorithm shows that both deliver good and similar results for the far range measurements but the runtime of the FBP algorithm is up to 150 times longer as the 2D-FFT runtime. In the near range measurements the FBP algorithm displays a very good azimuth resolution and targets which are very close to each other can be separated easily. In contrast to that the 2D-FFT algorithm has a lower azimuth resolution in the near range, thus targets which are very close to each other, merge together and cannot be separated. [ABSTRACT FROM AUTHOR]

Forward-looking imaging and target detection are highly desirable in many military and civilian fields, such as search and rescue, sea surface surveillance, airport surveillance, and guidance. However, there is a blind zone of forward-looking imaging for conventional Doppler beam sharpening and synthetic aperture radar. Scanning radar can be utilized to obtain a real beam image of a forward-looking area and implement target detection, while its azimuth resolution is poor due to the limitation of antenna size. Besides, during the processing procedure, imaging and target detection are usually regarded as two independent parts, which means that the imaging result will directly affect the detection performance. In this article, an integrated algorithm of super-resolution imaging and target detection for forward-looking scanning radar is proposed. In this algorithm, first of all, low-rank and sparse constraints as regularization norms are incorporated into the forward-looking scanning radar imaging and the objective function is established. Subsequently, the convex theory is utilized to solve the objective function and transform the problem of simultaneous super-resolution imaging and target detection into an optimization problem. Lastly, the super-resolution imaging and the target detection results are obtained simultaneously by solving the optimization problem using the alternating direction method of multipliers. In addition, simulation and experiment results are given to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The piecewise constant Doppler (PCD) algorithm is a novel radar imaging process recently proposed for the generalized continuous-wave synthetic aperture radar (GCW-SAR). This article presents a detailed theoretical analysis on the PCD algorithm's performance and proposes a further complexity-reduced PCD algorithm with motion compensation (MOCO) suitable for practical applications. First, the difference between conventional SAR imaging and PCD imaging, i.e., the zeroth-order versus the first-order slant range approximation, is revealed. Exact ambiguity function expressions of the PCD imaging in range and azimuth directions, respectively, are then derived. An error function of the PCD imaging as compared with the ideal matched filtering method is further defined and shown to be a function of an image quality factor, which can be used to quantify the PCD imaging performance. Finally, a faster and more flexible imaging process, called decimated PCD algorithm, is proposed, by which the image azimuth spacing can be easily extended, and hence, the computational complexity can be significantly reduced. The decimated PCD implementation incorporated with the MOCO is developed for practical GCW-SAR applications, and its imaging error lower-bounded by the PCD imaging error function is analyzed accordingly. Simulation and experimental results validate the theoretical analysis of the PCD imaging and show that the decimated PCD algorithm can achieve a high imaging quality at low cost. [ABSTRACT FROM AUTHOR]

In synthetic aperture (SA) imaging reported in the ultrasound imaging literature, typically, the delay and sum (DAS) beamformer is used; however, it is computationally expensive due to the pixel-by-pixel processing performed in the time domain. Recently, the adaptation of frequency-domain beamformers for medical ultrasound SA imaging, particularly to single-element/multielement synthetic transmit aperture (STA/MSTA) schemes, has been reported. In such reports, usually, less attention is paid to reducing system complexity. Recently, a sparse-transmit sparse-receive version of diverging beam-based synthetic aperture technique (DBSAT) was shown to achieve a reduction in system complexity by using fewer parallel receive channels, yet it achieves better quality and higher frame rate than conventional focused beamforming. However, this was also demonstrated using the DAS beamformer. In this work, we aim at achieving a reduction in computational cost, in addition to a reduction in system complexity, by implementing a fast and efficient frequency–wavenumber ($\omega $ - ${k}$) algorithm for the sparse DBSAT scheme. In doing so, an additional novel step of recovering missing frame data due to sparse transmit is introduced, namely, projection onto elliptical sets (POES). The results from this novel combination of $\omega $ - ${k}$ with POES recovery showed that it is feasible to achieve several orders of magnitude faster reconstruction compared with the standard DAS beamforming, without any compromise in the image quality and, in some cases, with improved image quality. The average value of the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) calculated from cyst at 15-mm depth obtained using the different schemes was 4.94 and 5.73 dB better when $\omega $ - ${k}$ was employed instead of DAS, respectively. In addition, for the sparse data set acquired with a 50% overlap during transmit and 64 active receive elements, DAS reconstruction takes as long as ~647 s, whereas the $\omega $ - ${k}$ algorithm takes only ~2 s when programmed and executed in MATLAB. [ABSTRACT FROM AUTHOR]

A fast synthetic aperture (SA) focusing algorithm is proposed in this article to overcome the problem of huge computational complexity in developing high-performance SA ultrasound imaging systems. The proposed method realizes the fast focusing of SA imaging by utilizing a virtual array (VA) technique in combination with nonuniform fast Fourier transform (VA-NUFFT) method. The computational performance of the VA-NUFFT algorithm is evaluated with the computational complexity under acceptable beam errors, compared with the conventional delay-and-sum (DAS) method and the frequency-domain direct method (DM), as well as a multiple NUFFT (M-NUFFT) method. Simulation results reveal that under a beam error level less than −57.4 dB, 25 and 10 reduction ratios of entire real operations can be achieved by the VA-NUFFT at larger SA scan lines of 2048, when compared with the DAS and DM, respectively. In the key SA focusing step, i.e., the “spatial process” step, the computational load of VA-NUFFT is lower than two orders of magnitude of that of the DM. SA imaging of a phantom with four cysts is also simulated and a contrast-to-noise ratio (CNR) criterion is adopted for evaluation of imaging quality. CNR results reflect that SA images of using VA-NUFFT make almost no difference to that of DAS focusing under different signal-to-noise ratios (SNRs). [ABSTRACT FROM AUTHOR]

Clutter background suppression and velocity estimation for moving targets are two critical problems in synthetic aperture radar–ground moving target indication (SAR-GMTI). A robust principal component analysis (RPCA) method is used to separate the sparse matrix of moving targets from the low-rank matrix of static backgrounds by using amplitude information in the image domain. However, the nonsparsity of the moving target echoes limits the performance of the RPCA in SAR-GMTI, and the velocity of the moving target cannot be estimated since the phase information is destroyed by the soft-thresholding operator in the RPCA process. To solve these problems, a novel moving target detection method based on RPCA (NRPCA) for SAR systems is proposed in this article. An atomic norm-based optimization program is first constructed to transform the data sparsity requirement into a moving target sparsity requirement. Although this optimization program is NP-hard, it is transformed to semidefinite programming by relaxation. Furthermore, accurate velocity estimation is performed using dual function theory and the alternating direction method of multipliers (ADMM) algorithm while the selection of the sparsity order k is avoided. Simulations and analyses based on experimental data illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, an atmospheric phase screen (APS) compensation algorithm for a near real-time ground-based interferometry synthetic aperture radar (GB-InSAR) over a mountainous area is investigated. A novel APS compensation scheme is proposed to compensate the fluctuated APS caused by a spatial 3-D inhomogeneous refractivity index distribution without any a priori knowledge of moving location. The proposed method simultaneously addresses to identify moving pixels by a criterion of absolute velocity estimated by the coherent pixels technique (CPT). The proposed method consists mainly of three steps: 1) the stratified APS compensation; 2) identification of moving pixel candidate; and 3) the residual APS [remained APS after 1)] compensation by Kriging interpolation. The steps mentioned above are iteratively applied in order to increase the accuracy of the whole process. In this framework, we develop the 2-D quadratic polynomial model of the refractivity index with respect to slant range and topographic height for modeling the stratified APS. Furthermore, a prediction of the residual APS is achieved by applying the intrinsic random function of order $k$ (IRF- $k$) Kriging interpolation, taking into account the nonstationarity of the residual APS. We evaluate the proposed method using zero-baseline GB-differential InSAR (GB-DInSAR) data over a mountainous area located in Minami-Aso, Kumamoto, Japan, through the near real-time post-landslide measurement campaign. [ABSTRACT FROM AUTHOR]

The curved trajectory and long synthetic aperture time of medium-Earth-orbit (MEO) synthetic aperture radar (SAR) lead to a 2-D spatial variation in the signals. Traditional methods treat the range and azimuth variations separately and usually suffer from high computational complexities. In this article, we investigate the Doppler rate distribution across a large scene and exploit an optimal imaging coordinate system, in which the MEO SAR signals satisfy the azimuth-shift-invariant property. Thus, the additional processing of the azimuth spatial variation in MEO SAR imaging algorithms can be avoided, and the efficiency of the image formation processor can be obviously improved. The Doppler linearization is used to address the higher-order Doppler parameters to achieve more precise focusing, and at the same time, addresses the azimuth time shift caused by the changes of signal distribution. Finally, processing results of simulated stripmap-mode data with the 2-m resolution are presented to validate the proposed algorithm. [ABSTRACT FROM AUTHOR]

The recent boost in undersea operations has led to the development of high-resolution sonar systems mounted on autonomous vehicles. These vehicles are used to scan the seafloor in search of different objects such as sunken ships, archaeological sites, and submerged mines. An important part of the detection operation is the segmentation of sonar images, where the object’s highlight and shadow are distinguished from the seabed background. In this paper, we focus on the automatic segmentation of sonar images. We present our enhanced fuzzy-based with Kernel metric (EnFK) algorithm for the segmentation of sonar images which, in an attempt to improve segmentation accuracy, introduces two new fuzzy terms of local spatial and statistical information. Our algorithm includes a preliminary de-noising algorithm which, together with the original image, feeds into the segmentation procedure to avoid trapping to local minima and to improve convergence. The result is a segmentation procedure that specifically suits the intensity inhomogeneity and the complex seabed texture of sonar images. We tested our approach using simulated images, real sonar images, and sonar images that were created in two different sea experiments, using multibeam sonar and synthetic aperture sonar. The results show accurate segmentation performance that is far beyond the state-of-the-art results. [ABSTRACT FROM AUTHOR]

Abstract—Ultrasonic nondestructive testing has been widely used in the detection and evaluation of fatigue cracks and defects of high-speed railway. In order to improve the detection speed of cylindrical components such as rims, a theoretical model of the forward vector algorithm of ultrasonic immersion curved surface is established. For improving image quality, an averaging correction factor is introduced in this paper to avoid multiple irradiation of the same pixel by the same element, and the visualization of cylindrical components with internal defects is realized by simulation and experiment. The traditional synthetic aperture focusing technique (SAFT), Newton iterative synthetic aperture (NISA) and ultrasonic forward vector algorithm (UFVA) are compared and analyzed on the aspects of imaging effect, array performance index (API) and imaging efficiency. Compared with the other two algorithms, the ultrasonic forward vector algorithm can effectively overcome the iteration of calculation of the refraction point and improve the imaging efficiency. The efficiency is improved by 2 and 1 orders of magnitude respectively while imaging quality is guaranteed. At the same time, this algorithm plays an important role on the measurement of the inner and outer diameter of the rim, the defect detection, and the quantitative analysis of the surface wear, which provides an effective means for the rapid detection of internal defects of the regular curved parts. [ABSTRACT FROM AUTHOR]