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The self-tapping screw connectors are widely used in Cold-formed Steel (CFS) construction due to their rapid fastening with automatic power driving and ease of installation via a self-drilling process. However, there is a lack of a robust numerical method for the modelling of behaviours of screw fasteners bearing against steel sheets, where the connections can undergo different failure modes including fracture, bearing, tilting, pull-out and shear failure, particularly for CFS thin plates. A nonlinear explicit dynamic finite element (FE) model is developed in this paper to address this issue by validating the modelled load-deformation relationships and failure modes with available tests in literature. The proposed numerical model is improved in two aspects comparing to available research: firstly, a ductile fracture criterion was proposed that could address the fracture of ply metals subjected to complex stress state with less trial-and-error process. Secondly, the threads in the screw fasteners and ply sheets were carefully modelled where the inter-locking capacity between the screws and steel sheets were better taken into account, eliminating the need for an artificial friction factor. The FE modelled results were compared against the test results and showed good agreement in terms of connection strength and failure modes, including fracture, bearing, tilting, pull-out and shear failure. Finally, parametric studies were carried out to investigate the influence of steel sheet thickness and the screw outer diameter on the force–displacement relationships of the steel lap joints.

Scanning radar can be used to obtain images of targets in forward-looking area, and has attracted much attention in many fields, such as ocean monitoring, air-to-ground attack, navigation, and so on. However, its azimuth resolution is extremely poor due to the limitation of the antenna size. In order to break through the limitation, many superresolution algorithms have been proposed, and iterative shrinkage-thresholding algorithm (ISTA) is one of the most famous methods because of its antinoise ability and simplicity. In the meantime, the slow convergence of iterative shrinkage-thresholding algorithm is also known to all. In this article, a second-order accelerated ISTA for scanning radar forward-looking superresolution imaging is proposed. In this algorithm, a prediction vector is constructed before each iteration by using the first and the second-order difference information of iteration vectors to reduce the number of iterations and get a faster convergence speed. In the end, simulations and experimental results are given to illustrate the effectiveness of the accelerated imaging algorithm.

The problem of low azimuth resolution has restricted the applicability for radar forward-looking imaging in practice. In this paper, a sparse with fast majorization-minimization (SFMM) superresolution algorithm was proposed to realize fast superresolution imaging of sparse targets in radar forward-looking area. First, we analyzed the azimuth signal of the radar forward-looking area and modeled the azimuth signal as a convolution of antenna pattern and targets distribution. Second, the superresolution problem was converted into an L1 regularization issue by introducing the L1 norm to represent the distribution of the targets under the regularization framework. Third, according to the principle of majorization-minimization (MM) algorithm, a simple L2 regularization issue was obtained to replace the difficult L1 one, and the real target distribution was obtained by solving the L2 regularization problem (We named it sparse with MM (SMM) superresolution algorithm for convenience). Then, in order to improve the computational efficiency of the algorithm, we adopted the second-order vector extrapolation idea to accelerate the conventional MM algorithm and solve the L2 regularization problem. The simulation and real data verified that the proposed SFMM algorithm not only improves the azimuth resolution in radar forward-looking imaging but also increases convergence speed on the basis of SMM superresolution algorithm.

In general, autofocus methods integrated with frequency-domain imaging algorithms are instrumental to obtain a well-focused bistatic forward-looking synthetic aperture radar (BFSAR) image in the presence of motion errors. Nevertheless, before applying autofocus methods to correct the azimuth phase errors, range cell migration (RCM) should be eliminated by the RCM correction (RCMC) procedure in frequency-domain imaging algorithms. With motion errors being taken into account, there always exists some residual nonsystematic RCM (NsRCM), which refers to the residual migration components after the RCMC procedure. For the conventional side-looking SAR, NsRCM is caused by motion errors. On the other hand, NsRCM of BFSAR is originated from motion errors before RCMC and the NsRCM amplified by the RCMC procedure. In this paper, we analyze the different types of NsRCM in BFSAR imaging and their relationship. Based on the analyses, we propose an autofocus NsRCM correction scheme for BFSAR imagery using frequency-domain imaging algorithms that can eliminate the range-dependent NsRCM. The proposed scheme consists of three steps. First, for the BFSAR data after range compression and RCMC, a division procedure is carried out in the azimuth direction. The subblocks with the highest signal-to-clutter ratio along the range direction are selected after the azimuth segmentation procedure. Second, for the selected subblocks, the total NsRCM is estimated based on the minimum-entropy criterion. Based on the estimation results, different parts of the NsRCM are obtained by solving an ordinary differential equation. Third, a two-step compensation of the NsRCM is executed to reach the spatially variant correction. Simulations and experimental results are provided to demonstrate that our proposed scheme is effective for BFSAR imaging.

This paper uses the linear-elastic Theory of Critical Distances (TCD) to verify and quantify the analysis of a particular central fracture mode occurring in the classical double-cracked flat plate specimen subjected to mixed mode I/mode II loading. Analytical formulae for estimating the critical loading condition for such a special central fracture mode of double-cracked flat plate specimen are derived using both standard elastic beam theory and the TCD. The influence of the crack length, specimen geometry and material parameters on the critical loading angle for such a special central fracture mode is discussed in detail. Finally, in order to verify the proposed central fracture prediction formulae, linear elastic stress analyses for two double-cracked flat plate specimens made of different materials and having different loading angles were conducted using finite element analysis. The results in this study confirm the possibility of interior fracture modes and provide insight on the mechanism of analogous interior fracture occurring in practical applications.

Fiber-reinforced polymer (FRP) bar-reinforced unsaturated polyester resin concrete (UPC) can solve the problem of rebar corrosion in ordinary reinforced concrete members. However, it has not been widely used in engineering practice because there have been few studies conducted on the bond behaviors of FRP bar and UPC, and the interaction mechanisms between FRP bar and UPC have not been well understood. A series of pull-out tests are conducted on FRP bar-UPC specimens to study the bond behaviors between these two materials. Parametric studies are also carried out to investigate the effects of FRP bar diameter, fiber type, type of surface treatment, concrete cover thickness, and interfacial bond length between the two. Three failure modes of the specimens are observed from pull-out tests, i.e., FRP bar pull-out, tensile failure of FRP bar, and UPC split. A new constitutive model is, therefore, proposed to predict the bond stress of FRP bar and UPC in the residual stage, and the proposed model is finally verified by test data reported in this study.

Total variation (TV) method has been utilized to realize super-resolution and preserve contour information of target in radar forward-looking imaging. However, its real-time ability is restricted to matrix inversion. In this paper, a fast TV (FTV) superresolution method is proposed to improve the real-time superresolution ability of traditional TV method. The proposed FTV method utilizes the low displacement rank features of Toplitz matrix and realizes fast matrix inversion by Gohberg-Semencul (GS) representation. It not only effectively improves the azimuth resolution and preserve the contour information of target, but also reduced the computational complexity of traditional TV method to improve its real-time superresolution ability. The superior performance of the proposed FTV method is verified by simulation and measured data processing.

Sparse regularization method has been widely used to realize super-resolution imaging in radar forward-looking imaging. However, most of existed methods directly minimize a nondifferentiable L1 regularization problem. In this paper, a Majorize-Minimization (MM) based super-resolution method is proposed to realize super-resolution for radar forward-looking imaging. According to MM principle, the proposed method converts the non-differentiable L1 regularization problem into a differentiable L2 regularization problem, and the real target distribution is obtained by solving the L2 regularization problem. Due to the introduction of the sparse prior, the proposed method can better improve the azimuth resolution of radar forward-looking imaging. In addition, the application of MM principle makes the non-differentiable L1 regularization easier to be solved. Finally, the superior performance of the proposed method is verified by simulation.

An efficient echo simulation algorithm considering topography in time domain for bistatic SAR is proposed in this paper. By using subaperture processing and equivalent scatter, this method can implement efficient echo simulation. At last, the effectiveness of this method is verified by numerical simulation.

In this paper, an improved target extraction scheme is proposed for forward-looking scanning radar. In the scheme, an image with good azimuth resolution is obtained firstly by deconvolution technique, and then it is used to map a patch-image. Secondly, low rank and sparse matrix decomposition is conducted on the patch image. Thirdly, constant false-alarm rate (CFAR) detection method is used to extract the targets from the sparse matrix. Finally, simulation results are given to verify the effectiveness of the proposed scheme.

Due to the limitation of antenna size, the azimuth resolution of radar forward-looking imaging is limited. Conventional technologies improved the azimuth resolution and obtained good effects, but they are poor in contour recovery of targets. In this paper, we employed the total variation (TV) superresolution method to improve the azimuth resolution and recover the contour of the targets in radar forward-looking imaging. Firstly, the signal model of radar forward-looking imaging was deduced. Secondly, the TV operator was employed as the penalty term under regularization framework. Thirdly, the TV regularization model was solved by split Bregman algorithm. The TV regularization method not only improved the azimuth resolution, but also better recovered the contour of the targets. The performance was verified by simulations lastly.

Deconvolution techniques provide efficient implementations for super-resolution imaging for forward-looking scanning radar. However, deconvolution is normally an ill-posed problem, and the solution is extremely sensitive to noise. From a statistical perspective, maximum likelihood (ML) methods are able to condition the ill-posed problem into a well-posed one. Nevertheless, traditional ML methods only consider the amplitude of the echo and by ignoring the phase that do not adequately model the radar imaging system. In this letter, an I/Q-channel modeling ML method is proposed for forward-looking scanning radar. First, the probability model of the echo is deduced by jointly considering noise in the I and Q channels. Then, a probability density function of the received data is deduced and used to formulate the likelihood function. Finally, the targets can be precisely estimated by maximizing this likelihood function. The results of simulations and experiments are provided to illustrate the effectiveness of the proposed method.

High-resolution scanning radar mapping of the surface is an effective tool for addressing concerns in local environmental and social investigation fields. Regrettably, the azimuth resolution of a scanning radar is constrained by the antenna beamwidth. Multiple super-resolution approaches have been applied to the scanning radar to enhance the azimuth resolution, but they suffer from limited resolution improvement. In this paper, a methodology to derive surface estimates from the scanning radar at an improved azimuth resolution is proposed. We first consider the truncated spectrum by discarding the unreliable frequencies to suppress the noise amplification. Then, based on the iterative adaptive approach (IAA), a novel inverse filtering method is formulated to obtain lower sidelobes and a higher resolution. Finally, by taking advantage of the Fourier property of the steering matrix and the Toeplitz structure of the covariance matrix, we exploit the Gohberg-Semencul representation and the data-dependent trigonometric polynomials to derive a fast IAA (FIAA)-based inverse filtering to mitigate the computational burden. Simulation results and real data processing demonstrate that the proposed FIAA-based inverse filtering outperforms the existing super-resolution approaches in resolution improvement and results in a higher computational efficiency.

Developing highly efficient, reusable, non-toxic and low-cost catalysts is of great importance for persulfate-based advanced oxidation processes (AOPs). In this work, ferrocene was mixed into paraffin to prepare a candle, and the iron-containing candle soots were collected and heated at 500 °C~900 °C under N2 atmosphere for 1 h to prepare magnetically recyclable Fe0/Fe3O4@porous carbon (Fe0/Fe3O4@PC) catalysts. The Fe0/Fe3O4@PC-700 obtained after pyrolysis at 700 °C exhibited the best catalytic activity for sulfamethoxazole (SMX) degradation. 10 mg/L SMX could be completely degraded within 10 min by 0.2 g/L of Fe0/Fe3O4@PC-700 and 0.5 mM PMS at pH 5.0. The carbon shell effectively inhibited the Fe leaching of Fe0/Fe3O4@PC-700, and 99.73% of Fe was retained after five consecutive cycles. In the Fe0/Fe3O4@PC-700/PMS system, SMX was degraded through the sulfate radical (SO4·¯), hydroxyl radical (·OH), superoxide radical (O2·¯) dominated radical pathway, and the singlet oxygen (1O2) dominated non-radical pathway. The coexisting inorganic ions and natural organic matters (NOM) in actual water inhibited the degradation of SMX. Finally, four possible degradation pathways were proposed based on the degradation intermediates of SMX. This work provides a facile heat treatment of iron-containing candle soots strategy to prepare the metal@carbon catalysts for PMS-based AOP.

The Fifth Nanjing Yangtze River Bridge had adopted accelerated bridge construction utilizing precast systems to connect the prefabricated plate with the cast-in-place ultra-high performance concrete (UHPC) wet joint. Wet joints are prone to crack under fatigue load. In this study, the steel wire mesh (SWM) treatment method was adopted to enhance the interface crack resistance, and add coarse aggregate (CA) to UHPC to reduce cost. Decks were tested under static and fatigue loadings to study the effects of CA, SWM interface treatment, and stress level on fatigue performance. Results indicate that the fatigue performance of wet joints using UHPC with CA was similar to that using regular UHPC. Dovetail wet joints using SWM interface treatment effectively guarantee the bridging effect of the fiber at the interface and disperse fatigue cracks. Overloading affected the fatigue performance, and the fatigue damage was linear to the upper limit of stress when the stress ratio was fixed. Secant stiffness was used to evaluate the fatigue damage of decks, and the calculated damage was in good agreement with the actual bearing capacity loss. This study verified that the innovative dovetail wet joint is practical, economical, and guarantees fatigue performance.

In bistatic forward-looking synthetic aperture radar (BFSAR), conventional autofocus algorithms cannot estimate the phase errors accurately when the range walk is compensated in the azimuthal time domain. This problem stems from the influence of the azimuth-variant Doppler coefficients after linear range cell migration correction in azimuthal time domain. In principle, nonlinear chirp scaling processing can be carried out to remove the azimuth variance. Nevertheless, Doppler azimuth variations of the Doppler parameters induced by the motion errors are not taken into consideration and serious defocus would emerge in the final image. To cope with such a problem, an estimation–evaluation–equalization scheme is proposed before conventional autofocus for BFSAR. Different from the conventional autofocus method, the azimuth-variant Doppler coefficients are additionally estimated and equalized before autofocus, and as a consequence, phase errors can be precisely estimated. The BFSAR data processing results demonstrate the validity of the proposed method on the improvement of autofocus in BFSAR.

Bistatic forward-looking synthetic aperture radar (BFSAR) is a kind of bistatic SAR system that can image forward-looking terrain in the flight direction of a moving platform. In BFSAR, compensation of the flight trajectory errors is of great significance to get a well-focused image. To accomplish an accurate motion compensation in image processing, a high-precision navigation system is needed. However, in many cases, due to the accuracy limit of such systems, flight trajectory errors are hard to be compensated correctly, causing mainly the resolution decrease in final images. In order to cope with such a problem, we propose a rise-dimensional modeling and estimation for flight trajectory error based on raw BFSAR data in this paper. To apply this method, we first carry out a preprocessing named azimuth-slowtime decoupling to deal with the spatially variant flight trajectory error before estimation. Then, an optimization model for flight trajectory estimation under the criterion of maximum image intensity is built. The solution to the optimization model is the accurate flight trajectory. Then, block coordinate descent technique is used to solve this optimization model. The processing of BFSAR data shows that the algorithm can obtain a more accurate estimation results, and generate better focused images compared with the existing trajectory estimation method.

With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Owing to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. In BFSAR, the compensation of the spatially variant motion errors is of great significance to get a well-focused image. In this paper, first, the spatial-variance properties of motion errors are analyzed analytically and quantitatively. Different from the side-looking monostatic and bistatic SAR, 2-D space-variant motion errors should be taken into consideration in BFSAR. The 2-D spatial variance of the motion errors can be categorized into two parts, range-variant motion errors of the transmitter and azimuth-variant motion errors of the receiver. Moreover, these two parts are independent of each other. Based on this property analysis, second, a motion compensation (MoCo) approach with cubic-order processing is proposed to deal with the spatially variant motion errors in BFSAR. In the cubic-order processing, the first-order MoCo is performed to correct the spatially independent motion errors on the raw data. The second-order MoCo is accomplished on the non-range-cell-migration (RCM) data to deal with the range-variant errors. After the second-order MoCo, since the signal direction of the non-RCM data coincides with the variant direction of the uncompensated phase errors, the azimuth-variant motion errors and slow time signal are coupled together. To cope with such a problem, the slow time signal is transformed into the direction perpendicular to the azimuth by a novel procedure named azimuth–slow time decoupling. At this stage, the coupling between the azimuth-variant motion errors and slow time signal has been eliminated. Azimuth-variant motion errors can be corrected precisely. Simulation and experimental results verify the effectiveness of the proposed method.

Target detection is an important function of forward-looking scanning radar search and tracking applications. However, it is difficult to detect the targets using the real beam image with low azimuth resolution. In this paper, a target detection scheme is proposed for forward-looking scanning radar. First, an image with better resolution is obtained by deconvolution technique, and it is used to map a patch-image. Then, according to the low rank characteristic of the patch-image and the sparse characteristic of the targets, the target detection is converted into an optimization problem of low rank and sparse matrix decomposition. Finally, the targets are obtained by solving this optimization problem. Simulations are given to verify its effectiveness.

Time and frequency synchronization is a crucial problem for Bistatic SAR systems. In this paper, frequency reference error of bistatic SAR system is analyzed firstly. Then the time and frequency synchronization errors are deduced and the intrinsic relationship between them is revealed. At last the effect of the frequency reference error is studied. Simulation and experiment results are given to verify the effectiveness of the analysis.

The clear contour is required when realize azimuth superresolution of forward-looking radar imaging in many applications. Traditional deconvolution methods achieve the azimuth superresolution but are limited in contour recovery. Although the total variation (TV) method can be used to keep the contour information, it’s sensitive to noise because of derivation. In this paper, we propose an improved total variation (ITV) method to realize azimuth superresolution of forward-looking radar imaging and recover the contour information. Firstly, the TV norm and L 2 norm are combined as the penalties under regularization framework. Then the regularization problem is solved by split Bregman algorithm. The proposed ITV method achieves higher azimuth resolution and better contour recovery performance than traditional methods, and the super performance is verified by simulations lastly.

Micro-Doppler signals analysis has been emerging as an important topic in target identification, and relative research has been focusing on features extraction and separation of the radar signals. As a time-frequency representation, the Hilbert-Huang transform (HHT) could extract the accurate instantaneous micro-Doppler signature from the radar signals by empirical mode decomposition and Hilbert transform. However, HHT has the shortcoming that it cannot decompose the signals with close-frequency components. To solve this problem, an innovative decomposition method for multicomponent micro-Doppler signals based on Hilbert–Huang transform and analytical mode decomposition (HHT-AMD) is proposed. In this method, the multicomponent micro-Doppler signals are firstly decomposed by empirical mode decomposition, and the decomposed signal components are transformed by Hilbert transform to get the Hilbert-Huang spectrum and marginal spectrum. Through the spectrum processing, we get the frequency distribution of each signal component. The next step is to judge whether there exists frequency aliasing in each signal component. If there is aliasing, the AMD method is used to decompose the signal until all the decomposed signals are mono-component signals. Evaluation considerations are covered with numerical simulations and experiments on measured radar data. The results demonstrate that compared with conventional HHT, the proposed method yields accurate decomposition for multicomponent micro-Doppler signals and improves the robustness of decomposition. The method presented here can also be applied in various settings of non-stationary signal analysis and filtering.

In this letter, an improved regularized iterative adaptive approach (IAA) is proposed for scanning radar angular superresolution. Because the IAA requires matrix inversion, the increasing condition number of the covariance matrix leads to the ill-posed problem of the IAA. Based on this reality, the diagonal loading method is introduced to solve the ill-posed problem. Because the loading value controls the tradeoff between the azimuth resolution and noise amplification, we use the radiometer uncertainty principle to determine the optimum loading value. When compared with the existing angular superresolution approaches, the proposed regularized IAA is shown to provide significant resolution improvement. Numerical results illustrate the superior performance of the proposed regularized IAA.

The 5th Nanjing Yangtze River Bridge is a three-tower cable-stayed bridge with a main span of 600 m and a composite cross section consisting of a steel box girder- and precast ultra-high performance concrete (UHPC) deck slab. The precast UHPC slabs are connected using cast-in-place UHPC, and joints between the UHPC slabs are prone to cracking under negative bending moment. This paper investigates the flexural behavior of an innovative dovetail UHPC joint in seven UHPC slabs under negative bending moment. An innovative method using steel wire mesh is presented to enhance the interface between precast and cast-in-place UHPC at the joints. Test parameters of the UHPC slabs included interface treatment method, joint material, reinforcing bar overlapping form, and prestressing level. The steel wire mesh generated fibers that bridge the interface between the precast and cast-in-place UHPC, thus significantly enhancing the mechanical performance of the jointed UHPC slabs: (1) the nominal cracking strength was increased by 2.4 MPa; (2) the post-cracking stiffness was retained at about 80% of the initial stiffness; (3) the ultimate stiffness was retained at about 35% of the initial stiffness; and (4) closely-spaced multiple cracks occurred at the joints. A new ductility index defined as the ratio of the ultimate deflection and the flexural cracking deflection is proposed to characterize the post-cracking ductility. The research findings are useful in understanding and improving the flexural behaviors of UHPC bridge decks subjected to negative bending moment.

For high-quality synthetic aperture radar (SAR) processing, Doppler centroid estimation is an essential procedure that can help to solve the SAR platforms motion parameters conversely, construct azimuth matching function and is important for target location as well. In this paper, a Doppler centroid estimator based on phase center tracking is proposed for SAR. An evaluation function is designed to measure the estimated Dopper centroid accuracy. In order to improve the calculation accuracy and computational efficiency, the evaluation function is solved by binary stage optimization to estimate the Doppler centroid. Simulations validate the effectiveness of this method.

Regularization is an efficient technology for radar forward-looking imaging. Nevertheless, for high-speed moving platforms, the antenna pattern will normally be distorted, and therefore the imaging performance of the traditional regularization method will be seriously deteriorated. This paper proposes a space variant sparse regularization super-resolution imaging method for high-speed moving forward-looking scanning radar. By analyzing the time-variant relationship between the scanning angle and the sight angle, the pattern aberration is firstly analyzed. Then an efficient piecewise constant model is established for the sake of low computational complexity and restoring cost. Finally, the space variant sparse regularization method has been derived based on the aberrant model. Simulation experiments demonstrate that the proposed method can improve the super-resolution performance of the high-speed platforms more efficiently than the traditional regularization method.

Deconvolution provides an efficient technology to implement angular super-resolution for scanning radar forward-looking imaging. However, deconvolution is an ill-posed problem, of which the solution is not only sensitive to noise, but also would be easily deteriorate by the noise amplification when excessive iterations are conducted. In this paper, a penalized maximum likelihood angular super-resolution method is proposed to tackle these problems. Firstly, a new likelihood function is deduced by separately considering the noise in I and Q channels to enhance the accuracy of the noise modeling for radar imaging system. Afterwards, to conquer the noise amplification and maintain the resolving ability of the proposed method, a joint square-Laplace penalty is particularly formulated by making use of the outlier sensitivity property of square constraint as well as the sparse expression ability of Laplace distribution. Finally, in order to facilitate the engineering application of the proposed method, an accelerated iterative solution strategy is adopted to solve the obtained convex optimal problem. Experiments based on both synthetic data and real data demonstrate the effectiveness and superior performance of the proposed method.

The co-occurrence of cyanotoxins and taste-and-odor compounds are a growing concern for drinking water treatment plants (DWTPs) suffering cyanobacteria in water resources. The dissolved and cell-bound forms of three microcystin (MC) congeners (MC-LR, MC-RR and MC-YR) and four taste-and-odor compounds (geosmin, 2-methyl isoborneol, β-cyclocitral and β-ionone) were investigated monthly from August 2011 to July 2012 in the eastern drinking water source of Lake Chaohu. The total concentrations of microcystins and taste-and-odor compounds reached 8.86 μg/L and 250.7 ng/L, respectively. The seasonal trends of microcystins were not consistent with those of the taste-and-odor compounds, which were accompanied by dominant species Microcystis and Dolichospermum. The fate of the cyanobacteria and metabolites were determined simultaneously after the processes of coagulation/flocculation, sedimentation, filtration and chlorination in the associated full-scale DWTP. The dissolved fractions with elevated concentrations were detected after some steps and the breakthrough of cyanobacteria and metabolites were even observed in finished water. Chlorophyll-a limits at intake were established for the drinking water source based on our investigation of multiple metabolites, seasonal variations and their elimination rates in the DWTP. Not only microcystins but also taste-and-odor compounds should be taken into account to guide the management in source water and in DWTPs.

Fast Factorized Back-projection (FFBP) is a new way to fix the high computation problem in Back-projection (BP) but it still keeps high-resolution and strong adaptability in synthetic aperture radar (SAR). However, Applying conventional autofocus methods to both BP and FFBP requires a special consideration. In this letter, we present a new method to solve the phase error problem. We establish a evaluation function based on maximum image sharpness in FFBP. Then we utilize Coordinate Descent (CD) optimization to transform multi-variables into a single variable. To deal with the single variable problem we introduce a secant method. The proposed method keeps the rapid imaging performance of FFBP and well solves the problems caused by phase errors, and has faster convergence speed. Using the simulation data to verify the effectiveness of the proposed algorithm.

Various per- and poly-fluorinated compounds (PFCs) were first systematically investigated in North Canal Basin, Beijing, China. A total of 68 surface water samples were collected from North Canal Basin, Beijing, at high spatial resolution. The seasonal disparity was compared and associated with source variation. PFCs concentrations in low-water period ranged from 26 to 207 ng/L, and significantly declined levels were found in high-water period. The individual component proportions among different sites varied less in high-water period, when runoff played a role in mixing and diluting PFCs. A methodology combined with principal component analysis (PCA), heat map-hierarchical cluster analysis (HM-HCA), and correlation analysis were introduced to discriminate sources of PFCs in surface water. The statistical results agreed with each other, and daily domestic consumption, fire-fighting products and related industries were identified as sources of PFCs in this region. In addition, two composition ratios were proposed through the methodology to distinguish the impact of nonpoint source, and the outcome demonstrates that great disparities exist in compositional profiles between nonpoint source and others. Overall, the results showed that this comprehensive analysis method has great potential for source apportionment in surface water and other environmental compartments.

Petroleum Cyber-Physical System (CPS) marks the beginning of a new chapter of the oil and gas industry. Combining vast computational power with intelligent Computer Aided Design (CAD) algorithms, petroleum CPS is capable of precisely modeling the flow of fluids over the entire petroleum reservoir and leveraging the massive field data remotely collected at the production wells. It provides field operators with valuable insights into the geological structure and remaining reserves of the reservoir for optimizing their operational strategies. Despite such benefits, petroleum CPS is vulnerable to various cyberattacks that jeopardize the integrity of the field data collected at production wells. Given manipulated field data, CAD software would generate an inaccurate reservoir model which misleads the field operators. This work is the first to analyze potential cybersecurity attacks in a petroleum CPS. In this paper, an intelligent cyberattack strategy optimization framework is proposed to optimize the malicious manipulation of field data such that the history matching solver generates the most inaccurate reservoir model. Our method is based on the advanced Model Reference Adaptive Search (MRAS) technique, and it can be used to evaluate the worst case impact due to the field data manipulation attacks. Experimental results on a standard petroleum CPS testcase demonstrate that the proposed method can reduce the production quality, measured by the weighted mismatch sum of the bottom hole pressure (BHP), the gas oil ratio (GOR), and the Water Cut (WCT), by up to 99.1% when comparing to a random attack.

For bistatic forward-looking synthetic aperture radar (BFSAR), images are often blurred by uncompensated radar motion errors. To get refocused images, autofocus is a useful postprocessing technique. However, a severe drawback of the autofocus algorithms is that they are only capable of removing one-dimensional azimuth phase errors. In BFSAR, motion errors and approximations of imaging algorithms introduce residual range cell migration (RCM) on BFSAR data as well. When residual RCM is within a range resolution cell, it can be neglected. However, the residual migration, which exceeds a range cell, is increasingly encountered as resolution becomes finer and finer. A novel residual RCM correction method is proposed in this paper. By fitting the low-frequency phase difference of adjacent azimuth cells, residual RCM of each azimuth cell can be corrected precisely and effectively. Simulations and real data experiments are carried out to validate the effectiveness of the proposed method.

This paper proposes a novel registration method for optical and SAR images which is based on straight line features and mutual information. Firstly, different edge detectors are employed to detect the line segments in both optical and SAR images respectively. Then, through the Hough transform and a straight line fitting and filtration method, the main straight lines of each image are extracted and their intersections are obtained and taken as the candidate matching points. With the RANSAC (RANdom SAmpling Consensus) method, corresponding point pairs (CPPs) are found with these candidate points and a coarse registration between the heterogeneous images is implemented. At last, by using the mutual information of the separated patches generated from the coarse registered images, a fine registration result is finally achieved. The experiment with a pair of X-band air-borne SAR and optical images validates the efficiency and precision of the proposed method.

For bistatic forward-looking synthetic aperture radar (BFSAR), motion errors induce two adverse effects on the echo, namely, azimuth phase error and residual range cell migration (RCM). Under the presumption that residual RCM is within a range resolution cell, residual RCM can be neglected, and azimuth phase error can be compensated utilizing autofocus methods. However, in the case that residual RCM exceeds the range resolution, two dimensional defocus would emerge in the final image. Generally speaking, residual RCM is relatively small and can be neglected in monostatic SAR. While the unique characteristics of BFSAR makes the residual RCM exceeding range resolution cell inevitable. Furthermore, the excessive residual migration is increasingly encountered as resolutions become finer. To cope with such a problem, minimum-entropy based residual RCM correction method is developed in this paper. The proposed method eliminates the necessity of the parametric model when estimating the residual RCM. Moreover, it meets the practical needs of BFSAR owing to no requirement of exhaustive computation. Simulations validate the effectiveness of the proposed method.

Free of the constraints of orbit mechanisms, weather conditions and minimum antenna area, synthetic aperture radar (SAR) equipped on near-space platform is more suitable for sustained large-scene imaging compared with the spaceborne and airborne counterparts. Terrain observation by progressive scans (TOPS), which is a novel wide-swath imaging mode and allows the beam of SAR to scan along the azimuth, can reduce the time of echo acquisition for large scene. Thus, near-space TOPS-mode SAR (NS-TOPSAR) provides a new opportunity for sustained large-scene imaging. An efficient full-aperture imaging scheme for NS-TOPSAR is proposed in this paper. In this scheme, firstly, two-step processing (TSP) is adopted to eliminate the Doppler aliasing of the echo. Then, the data is focused in two-dimensional frequency domain (FD) based on Stolt interpolation. Finally, a modified TSP (MTSP) is performed to remove the azimuth aliasing. Simulations are presented to demonstrate the validity of the proposed imaging scheme for near-space large-scene imaging application.

Regularization technology is an efficient and robust method to implement forward-looking imaging of scanning radar, and the selection of regularization parameter is quite important for the imaging performance. When the imaging scene contains sparse strong targets as well as a weak background, different regularization parameters should be adopted. A dual-channel fast iterative shrinkage-thresholding (D-FIST) regularization algorithm is proposed. First, by setting a threshold with mean segmentation method, the strong targets are extracted from the echo to formulate the data of channel I, and the entire data are included in channel II. The L-curve method is adopted to determine the regularization parameter for two channels data, respectively. Then, the data are processed by the FIST algorithm with corresponding parameters. At last, the imaging result is obtained by combining the results of two channels. Simulation experiments show that the imaging results of D-FIST are better than that of direct method, especially in a low SNR situation.