Design of synthetic aperture radar low-intercept radio frequency stealth

Not confined to a certain point,such as waveform,this paper systematically studies the low-intercept radio frequency(RF)stealth design of synthetic aperture radar(SAR)from the system level.The study is carried out from two levels.In the first level,the maximum low-intercept range equation of the conventional SAR system is deduced firstly,and then the maximum low-intercept range equation of the multiple-input multiple-output SAR system is deduced.In the second level,the waveform design and imaging method of the low-intercept RF SAR system are given and verified by simulation.Finally,the main technical characteristics of the lowintercept RF stealth SAR system are given to guide the design of low-intercept RF stealth SAR system.

Micro-motion effect in inverse synthetic aperture radar imaging of ballistic mid-course targets

For ballistic mid-course targets,in addition to constant orbital motion,the target or any structure on the target undergoes micro-motion dynamics,such as spin,precession and tumbling.The micro-motion characteristics of the ballistic mid-course targets were discussed.The target motion model and inverse synthetic aperture radar(ISAR) imaging model for this kind of targets were built.Then,the influence of micro-motion on ISAR imaging based on the established imaging model was presented.The computer simulation to get mid-course target echoes from static darkroom electromagnetic scattering data based on the established target motion model was realized.The imaging results of computer simulation show the validity of ISAR imaging analysis for micro-motion targets.

A novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition

Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar（SAR） sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.

Inverse synthetic aperture radar range profile compensation of plasma-sheath-enveloped reentry object

The scattering points in a plasma sheath characterized with coupled velocities can cause pulse compression mismatching,which results in displacement and energy diffusion in the onedimension range profile.To solve this problem,we deduce the echo model of the plasma-sheathenveloped reentry object.By estimating the coupled velocities,we propose a compensation method to correct the defocus of an inverse synthetic aperture radar(ISAR)image in range dimension to improve the quality of the ISAR images.The simulation results suggest that the echoes from different regions of the surface of the reentry object have various coupling velocities,and the higher the coupled velocity,the more serious the displacement and energy diffusion in the range dimension.Our proposed method can correct the range dimension aberration.Two measurement metrics were used to evaluate the improvement of the compensation method.

Application of improved equivalent edge currents in synthetic aperture radar imaging

Target modeling and scattering function calculating are important prerequisites and groundwork for the synthetic aperture radar（SAR） imaging simulation.According to the difficult problems that normal methods cannot calculate the scattering function of electrically large object under the condition to wideband,an effective method of improved equivalent edge currents is presented and applied to SAR imaging simulation for the first time.This method improves calculating velocity and has relatively high precision.The concrete steps of applying the method are given.By way of the simulation experiment,the effectiveness of the method is verified.

Three-dimensional positions of scattering centers reconstruction from multiple SAR images based on radargrammetry

A method and procedure is presented to reconstruct three-dimensional(3D) positions of scattering centers from multiple synthetic aperture radar(SAR) images. Firstly, two-dimensional(2D) attribute scattering centers of targets are extracted from 2D SAR images. Secondly, similarity measure is developed based on 2D attributed scatter centers' location, type, and radargrammetry principle between multiple SAR images. By this similarity, we can associate 2D scatter centers and then obtain candidate 3D scattering centers. Thirdly, these candidate scattering centers are clustered in 3D space to reconstruct final 3D positions. Compared with presented methods, the proposed method has a capability of describing distributed scattering center, reduces false and missing 3D scattering centers, and has fewer restrictionson modeling data. Finally, results of experiments have demonstrated the effectiveness of the proposed method.

Power of SAR Imagery and Machine Learning in Monitoring Ulva prolifera:A Case Study of Sentinel-1 and Random Forest

Automatically detecting Ulva prolifera(U.prolifera)in rainy and cloudy weather using remote sensing imagery has been a long-standing problem.Here,we address this challenge by combining high-resolution Synthetic Aperture Radar(SAR)imagery with the machine learning,and detect the U.prolifera of the South Yellow Sea of China(SYS)in 2021.The findings indicate that the Random Forest model can accurately and robustly detect U.prolifera,even in the presence of complex ocean backgrounds and speckle noise.Visual inspection confirmed that the method successfully identified the majority of pixels containing U.prolifera without misidentify-ing noise pixels or seawater pixels as U.prolifera.Additionally,the method demonstrated consistent performance across different im-ages,with an average Area Under Curve(AUC)of 0.930(+0.028).The analysis yielded an overall accuracy of over 96%,with an aver-age Kappa coefficient of 0.941(+0.038).Compared to the traditional thresholding method,Random Forest model has a lower estima-tion error of 14.81%.Practical application indicates that this method can be used in the detection of unprecedented U.prolifera in 2021 to derive continuous spatiotemporal changes.This study provides a potential new method to detect U.prolifera and enhances our under-standing of macroalgal outbreaks in the marine environment.

Entropy function optimization for radar imaging

The convergence performance of the minimum entropy auto-focusing（MEA） algorithm for inverse synthetic aperture radar（ISAR） imaging is analyzed by simulation. The results show that a local optimal solution problem exists in the MEA algorithm. The cost function of the MEA algorithm is not a downward-convex function of multidimensional phases to be compensated. Only when the initial values of the compensated phases are chosen to be near the global minimal point of the entropy function, the MEA algorithm can converge to a global optimal solution. To study the optimal solution problem of the MEA algorithm, a new scheme of entropy function optimization for radar imaging is presented. First, the initial values of the compensated phases are estimated by using the modified Doppler centroid tracking （DCT）algorithm. Since these values are obtained according to the maximum likelihood （ML） principle, the initial phases can be located near the optimal solution values. Then, a fast MEA algorithm is used for the local searching process and the global optimal solution can be obtained. The simulation results show that this scheme can realize the global optimization of the MEA algorithm and can avoid the selection and adjustment of parameters such as iteration step lengths, threshold values, etc.

ADAPTIVE RIVER SEGMENTATION IN SAR IMAGES

There is difficulty for distinguishing of river and shadow in Synthetic Aperture Radar (SAR) images. A method of river segmentation in SAR images based on wavelet energy and gradient is proposed in this paper. It mainly includes two algorithms: coarse segmentation and refined segmen- tation. Firstly, The river regions are coarsely segmented by the wavelet energy feature,and then refined segmented accurately by the gradient threshold which is got adaptively. The experimental results show the validity of the method, which provides a good foundation for targets detection above the river.

MAXIMUM A POSTERIORI-BASED AUTOMATIC TARGET DETECTION IN SAR IMAGES

The paper presents an algorithm of automatic target detection in Synthetic Aperture Radar(SAR) images based on Maximum A Posteriori(MAP). The algorithm is divided into three steps. First, it employs Gaussian mixture distribution to approximate and estimate multi-modal histogram of SAR image. Then, based on the principle of MAP, when a priori probability is both unknown and learned respectively, the sample pixels are classified into different classes c = {target,shadow, background}. Last, it compares the results of two different target detections. Simulation results preferably indicate that the presented algorithm is fast and robust, with the learned a priori probability, an approach to target detection is reliable and promising.

Compressive Sensing Inverse Synthetic Aperture Radar Imaging Based on Gini Index Regularization

In compressive sensing(CS) based inverse synthetic aperture radar(ISAR) imaging approaches, the quality of final image significantly depends on the number of measurements and the noise level. In this paper, we propose an improved version of CSbased method for inverse synthetic aperture radar(ISAR) imaging. Different from the traditional l1 norm based CS ISAR imaging method, our method explores the use of Gini index to measure the sparsity of ISAR images to improve the imaging quality. Instead of simultaneous perturbation stochastic approximation(SPSA), we use weighted l1 norm as the surrogate functional and successfully develop an iteratively re-weighted algorithm to reconstruct ISAR images from compressed echo samples. Experimental results show that our approach significantly reduces the number of measurements needed for exact reconstruction and effectively suppresses the noise. Both the peak sidelobe ratio(PSLR) and the reconstruction relative error(RE) indicate that the proposed method outperforms the l1 norm based method.

A modified OMP method for multi-orbit three dimensional ISAR imaging of the space target

The conventional two dimensional(2D)inverse synthetic aperture radar(ISAR)imaging fails to provide the targets'three dimensional(3D)information.In this paper,a 3D ISAR imaging method for the space target is proposed based on mutliorbit observation data and an improved orthogonal matching pursuit(OMP)algorithm.Firstly,the 3D scattered field data is converted into a set of 2D matrix by stacking slices of the 3D data along the elevation direction dimension.Then,an improved OMP algorithm is applied to recover the space target's amplitude information via the 2D matrix data.Finally,scattering centers can be reconstructed with specific three dimensional locations.Numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed 3D imaging method.

SAR image denoising based on wavelet-fractal analysis

Wavelet-fractal based SAR （synthetic aperture radar） image processing is one of the advanced technologies in image processing. The main concept of analysis is that after wavelet transformation, multifractal spectrum of the signal is different from that of noise. This difference is used to alleviate the noise produced by SAR image.The method to denoise SAR image using the process based on wavelet-fractai analysis is discussed in detail. Essentially, the present method focuses on adjusting the Hoelder exponent α of multifractal spectrum. After simulation, α should be adjusted to 1.72-1.73. The more the value of α exceeds 1.73, the less distinctive the edges of SAR image become. According to the authors denoising is optimal at α=1.72-1.73. In other words, when α =1.72-1.73, a smooth and denoised SAR image is produced.

Edge Detection of River in SAR Image Based on Contourlet Modulus Maxima and Improved Mathematical Morphology

To cope with the problems that edge detection operators are liable to make the detected edges too blurry for synthetic aperture radar(SAR)images,an edge detection method for detecting river in SAR images is proposed based on contourlet modulus maxima and improved mathematical morphology.The SAR image is firstly transformed to a contourlet domain.According to the directional information and gradient information of directional subband of contourlet transform,the modulus maximum and the improved mathematical morphology are used to detect high frequency and low frequency sub-image edges,respectively.Subsequently,the edges of river in SAR image are obtained after fusing the high frequency sub-image and the low frequency sub-image.Experimental results demonstrate that the proposed edge detection method can obtain more accurate edge location and reduce false edges,compared with the Canny method,the method based on wavelet and Canny,the method based on contourlet modulus maxima,and the method based on improved(ROEWA).The obtained river edges are complete and clear.

SAR image de-noising via grouping-based PCA and guided filter

A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.

SAR imaging method based on coprime sampling and nested sparse sampling

As the signal bandwidth and the number of channels increase, the synthetic aperture radar （SAR） imaging system produces huge amount of data according to the Shannon-Nyquist theorem, causing a huge burden for data transmission. This paper concerns the coprime sampl which are proposed recently but ng and nested sparse sampling, have never been applied to real world for target detection, and proposes a novel way which utilizes these new sub-Nyquist sampling structures for SAR sampling in azimuth and reconstructs the data of SAR sampling by compressive sensing （CS）. Both the simulated and real data are processed to test the algorithm, and the results indicate the way which combines these new undersampling structures and CS is able to achieve the SAR imaging effectively with much less data than regularly ways required. Finally, the influence of a little sampling jitter to SAR imaging is analyzed by theoretical analysis and experimental analysis, and then it concludes a little sampling jitter have no effect on image quality of SAR.

ISAR cross-range scaling based on the MUSIC technique

Cross-range scaling plays an important role in the inverse synthetic aperture radar(ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation(FFT). However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity(RV) estimation of the slow rotation target. In this paper,we propose an accurate cross-range scaling algorithm based on the multiple signal classification(MUSIC) method. We first select some range bins with the mono-component linear frequency modulated(LFM) signal model. Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency. After processing all the range bins, a linear relationship related to the RV can be obtained. Eventually, the ISAR image can be scaled. The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity. Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data. Experimental results with the simulated and raw data validate the superiority of the novel method.

Fast ISAR imaging method based on scene segmentation

Although compressed sensing inverse synthetic aperture radar(ISAR) imaging methods are widely used in radar signal processing, its reconstructing time and memory storage space requirements are very high. The main reason is that large scene reconstruction needs a higher dimension of the sensing matrix. To reduce this limitation, a fast high resolution ISAR imaging method,which is based on scene segmentation for random chirp frequencystepped signals, is proposed. The idea of scene segmentation is used to solve the problems aforementioned. In the method,firstly, the observed scene is divided into multiple sub-scenes and then the sub-scenes are reconstructed respectively. Secondly, the whole image scene can be obtained through the stitching of the sub-scenes. Due to the reduction of the dimension of the sensing matrix, the requirement of the memory storage space is reduced substantially. In addition, due to the nonlinear superposition of the reconstructed time of the segmented sub-scenes, the reconstruction time is reduced, and the purpose of fast imaging is achieved.Meanwhile, the feasibility and the related factors which affect the performance of the proposed method are also analyzed, and the selection criterion of the scene segmentation is afforded. Finally,theoretical analysis and simulation results demonstrate the feasibility and effectiveness of the proposed method.

NEW SAR IMAGE INTERPRETATION METHOD OF AIRCRAFT BASED ON JOINT TIME-FREQUENCY ANALYSIS

With the continuous improvement of Synthetic Aperture Radar(SAR) resolution, interpreting the small targets like aircraft in SAR images becomes possible and turn out to be a hot spot in SAR application research. However, due to the complexity of SAR imaging mechanism, interpreting targets in SAR images is a tough problem. This paper presents a new aircraft interpretation method based on the joint time-frequency analysis and multi-dimensional contrasting of basic structures. Moreover, SAR data acquisition experiment is designed for interpreting the aircraft. Analyzing the experiment data with our method, the result shows that the proposed method largely makes use of the SAR data information. The reasonable results can provide some auxiliary support for the SAR images manual interpretation.

SAR image de-noising based on texture strength and weighted nuclear norm minimization

As synthetic aperture radar（SAR） has been widely used nearly in every field, SAR image de-noising became a very important research field. A new SAR image de-noising method based on texture strength and weighted nuclear norm minimization（WNNM） is proposed. To implement blind de-noising, the accurate estimation of noise variance is very important. So far, it is still a challenge to estimate SAR image noise level accurately because of the rich texture. Principal component analysis（PCA） and the low rank patches selected by image texture strength are used to estimate the noise level. With the help of noise level, WNNM can be expected to SAR image de-noising. Experimental results show that the proposed method outperforms many excellent de-noising algorithms such as Bayes least squares-Gaussian scale mixtures（BLS-GSM） method, non-local means（NLM） filtering in terms of both quantitative measure and visual perception quality.