A fast decoupled ISAR high-resolution imaging method using structural sparse information under low SNR

Inverse synthetic aperture radar (ISAR) image can be represented and reconstructed by sparse recovery (SR) approaches. However, the existing SR algorithms, which are used for ISAR imaging, have suffered from high computational cost and poor imaging quality under a low signal to noise ratio (SNR) condition. This paper proposes a fast decoupled ISAR imaging method by exploiting the inherent structural sparse information of the targets. Firstly, the ISAR imaging problem is decoupled into two sub-problems. One is range direction imaging and the other is azimuth direction focusing. Secondly, an efficient two-stage SR method is proposed to obtain higher resolution range profiles by using jointly sparse information. Finally, the residual linear Bregman iteration via fast Fourier transforms (RLBI-FFT) is proposed to perform the azimuth focusing on low SNR efficiently. Theoretical analysis and simulation results show that the proposed method has better performence to efficiently implement higher-resolution ISAR imaging under the low SNR condition.

Research on spatial-variant property of bistatic ISAR imaging plane of space target

The imaging plane of inverse synthetic aperture radar （ISAR） is the projection plane of the target. When taking an image using the range-Doppler theory, the imaging plane may have a spatial-variant property, which causes the change of scatter＇s projection position and results in migration through resolution cells, In this study, we focus on the spatial-variant property of the imaging plane of a three-axis-stabilized space target. The innovative contributions are as follows. 1） The target motion model in orbit is provided based on a two-body model. 2） The instantaneous imaging plane is determined by the method of vector analysis. 3） Three Euler angles are introduced to describe the spatial-variant property of the imaging plane, and the image quality is analyzed. The simulation results confirm the analysis of the spatial-variant property. The research in this study is significant for the selection of the imaging segment, and provides the evidence for the following data processing and compensation algorithm.

ISAR imaging based on improved phase retrieval algorithm

Traditional inverse synthetic aperture radar(ISAR)imaging methods for maneuvering targets have low resolution and poor capability of noise suppression. An ISAR imaging method of maneuvering targets based on phase retrieval is proposed,which can provide a high-resolution and focused map of the spatial distribution of scatterers on the target. According to theoretical derivation, the modulus of raw data from the maneuvering target is not affected by radial motion components for ISAR imaging system, so the phase retrieval algorithm can be used for ISAR imaging problems. However, the traditional phase retrieval algorithm will be not applicable to ISAR imaging under the condition of random noise. To solve this problem, an algorithm is put forward based on the range Doppler(RD) algorithm and oversampling smoothness(OSS) phase retrieval algorithm. The algorithm captures the target information in order to reduce the influence of the random phase on ISAR echoes, and then applies OSS for focusing imaging based on prior information of the RD algorithm. The simulated results demonstrate the validity of this algorithm, which cannot only obtain high resolution imaging for high speed maneuvering targets under the condition of random noise, but also substantially improve the success rate of the phase retrieval algorithm.

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.

Optimal ship imaging for shore-based ISAR using DCF estimation

The optimal imaging time selection of ship targets for shore-based inverse synthetic aperture radar （ISAR） in high sea conditions is investigated. The optimal imaging time includes opti- mal imaging instants and optimal imaging duration. A novel method for optimal imaging instants selection based on the estimation of the Doppler centroid frequencies （DCFs） of a series of images obtained over continuous short durations is proposed. Combined with the optimal imaging duration selection scheme using the image contrast maximization criteria, this method can provide the ship images with the highest focus. Simulated and real data pro- cessing results verify the effectiveness of the proposed imaging method.

Approach for ISAR imaging of near-field targets based on coordinate conversion and image interpolation

Inverse synthetic aperture radar(ISAR)imaging of near-field targets is potentially useful in some specific applications,which makes it very important to efficiently produce highquality image of the near-field target.In this paper,the simplified target model with uniform linear motion is applied to the near-field target imaging,which overcomes the complexity of the traditional near-field imaging algorithm.According to this signal model,the method based on coordinate conversion and image interpolation combined with the range-Doppler(R-D)algorithm is proposed to correct the near-field distortion problem.Compared with the back-projection(BP)algorithm,the proposed method produces better focused ISAR images of the near-field target,and decreases the computation complexity significantly.Experimental results of the simulated data have demonstrated the effectiveness and robustness of the proposed method.

Evaluation of Trajectory Error Effects in BP Based Space Target ISAR Imaging

The space target imaging is important in the development of space technology.Due to the availability of trajectory information of the space targets and the arising of rapid parallel processing hardware,the back projection (BP) method has been applied to synthetic aperture radar (SAR) imaging and shows a number of advantages as compared with conventional Fourier-domain imaging algorithms.However,the practical processing shows that the insufficient accuracy of the trajectory information results in the degrading of the imaging results.On the other hand,the autofocusing algorithms for BP imaging are not well developed,which is a bottleneck for the application of BP imaging.Here,an analysis of the effect of trajectory errors on the space target imaging using microlocal technology is presented.Our analysis provides an explicit quantitative relationship between the trajectory errors of the space target and the positioning errors in the reconstructed images.The explicit form of the position errors for some typical trajectory errors is also presented.Numerical simulations demonstrate our theoretical findings.The measured position errors obtained from the reconstructed images are consistent with the analytic errors calculated by using the derived formulas.Our results will be used in the development of effective autofocusing methods for BP imaging.

Target rotation parameter estimation for ISAR imaging via frame processing

Frame processing method offers a model-based approach to Inverse Synthetic Aperture Radar(ISAR) imaging. It also provides a way to estimate the rotation rate of a non-cooperative target from radar returns via the frame operator properties. In this paper, the relationship between the best achievable ISAR image and the reconstructed image from radar returns was derived in the framework of Finite Frame Processing theory. We show that image defocusing caused by the use of an incorrect target rotation rate is interpreted under the FP method as a frame operator mismatch problem which causes energy dispersion. The unknown target rotation rate may be computed by optimizing the frame operator via a prominent point. Consequently, a prominent intensity maximization method in FP framework was proposed to estimate the underlying target rotation rate from radar returns. In addition, an image filtering technique was implemented to assist searching for a prominent point in practice. The proposed method is justified via a simulation analysis on the performance of FP imaging versus target rotation rate error.Effectiveness of the proposed method is also confirmed from real ISAR data experiments.

Sparse Bayesian learning in ISAR tomography imaging

Inverse synthetic aperture radar(ISAR) imaging can be regarded as a narrow-band version of the computer aided tomography(CT). The traditional CT imaging algorithms for ISAR, including the polar format algorithm(PFA) and the convolution back projection algorithm(CBP), usually suffer from the problem of the high sidelobe and the low resolution. The ISAR tomography image reconstruction within a sparse Bayesian framework is concerned. Firstly, the sparse ISAR tomography imaging model is established in light of the CT imaging theory. Then, by using the compressed sensing(CS) principle, a high resolution ISAR image can be achieved with limited number of pulses. Since the performance of existing CS-based ISAR imaging algorithms is sensitive to the user parameter, this makes the existing algorithms inconvenient to be used in practice. It is well known that the Bayesian formalism of recover algorithm named sparse Bayesian learning(SBL) acts as an effective tool in regression and classification,which uses an efficient expectation maximization procedure to estimate the necessary parameters, and retains a preferable property of the l0-norm diversity measure. Motivated by that, a fully automated ISAR tomography imaging algorithm based on SBL is proposed.Experimental results based on simulated and electromagnetic(EM) data illustrate the effectiveness and the superiority of the proposed algorithm over the existing algorithms.

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 Moving Target Detection and Imaging Based on WVH Transform

Aim To propose a generalized and closed representation of the Wigner Ville Hough transform(WVHT), for the moving target detection and imaging in the design of synthetic aperture radar(SAR). Methods Based on the line integral, the WVH transform was derived by combining the Wigner Ville distribution (WVD) and the Hough transform (HT) together. The new transform was then verified with computer by the simulated SAR echoes. Results and Conclusion The correctness and the validity of the WVH transform were proved by the computer simulation. Compared with the conventional WVD HT method, the new approach based on the WVHT can simplify the processing procedure, it can translate the chirp echoes of multi targets of SAR from the time domain into the parameter space directly, while suppressing the cross terms of WVD and estimating the motion coefficients for the final imaging. It is obvious that the WVH transform can be also used in other cases for the chirp signal detection.

Improvedforward—lookingimagingmethodforairbornearrayFMCWSAR

机载阵列前视合成孔径雷达（SAR）信号模型中忽略载机速度会影响成像。针对该问题，在调频连续波（FMCW）SAR信号模型中考虑了载机速度，分析了栽机速度的影响和裁机连续运动引入的多普勒频率偏移的特点，结合FrequencyScaling算法，研究了机载阵列FMCWSAR前视成像。成像仿真和性能参数分析表明：改进方法可精确实现载机运动条件下的FMCWSAR前视成像。

基于特征提取的ISAR成像欺骗干扰评估方法研究

雷达成像欺骗干扰是雷达对抗装备的一种重要干扰手段,在弹载、机载等多种武器平台上具有广泛应用。在分析ISAR成像欺骗干扰技术的基础上,研究了ISAR图像特性的提取方法,提出了一种基于ISAR图像特征提取的雷达成像欺骗干扰效果评估方法,解决了雷达对抗装备成像欺骗干扰能力评估难题,可为相关雷达干扰对抗试验及评估等提供参考。

基于变成像平面的海面运动船舶ISAR成像方法

海面船舶目标在高海况下呈现复杂的六自由度运动。采用长合成孔径时间的ISAR成像可以提高船舶目标的方位向成像分辨率,有助于船舶目标的检测与识别。然而,长合成孔径时间内,船舶目标的有效转轴与雷达视线都会发生变化,成像平面也会发生变化,不符合ISAR二维转台模型假设。直接采用常规的ISAR成像算法可能导致船舶目标图像散焦。因此,为了提高合成孔径时间并增加方位向成像分辨率,本文提出了一种变成像平面成像算法,首先基于成像聚焦质量评估中常用的最小熵准则,估计船舶目标的转动参数,并根据转动参数调整成像平面,然后在调整后的成像平面上进行BP成像。仿真实验表明,所设计的算法能够突破ISAR二维转台目标成像的限制,增加合成孔径处理时间,并提高方位向成像分辨率。

GKPA-联合全局与关键区域的光学-SAR中分辨率遥感影像场景分类

遥感影像场景分类是自然灾害检测和城市功能规划等实际应用的基础,因此研究遥感影像场景分类具有重要意义。基于深度学习的方法由于其强大的特征提取能力已经成为遥感影像场景分类领域最常用的方法,此类方法强烈依赖数据集的质量。现阶段场景分类领域的数据集大多为高分辨率的光学影像,易于提取有效的语义特征。在面对中分辨率场景时,影像中细节信息更匮乏,有效特征不够显著,场景分类具有更大难度,相较于高分辨率场景分类精度不够理想。在此情况下,利用其他模态的数据如合成孔径雷达(SAR)提供互补特征可以有效改善场景分类精度。考虑到以上现状,本文构建光学-SAR遥感影像场景分类数据集(OS-RSISC),基于此数据集进一步提出了一个联合全局与关键区域的感知注意力双模态场景分类框架(GKPA-RSSC)。实验结果显示GKPA-RSSC相较于对比方法具有最高的分类精度,总体精度达81.97%。光学-SAR数据与单模态数据的对比试验结果突显双模态数据的优势,进而验证了本文提出的光学-SAR场景分类数据集的重要意义。

某型舰载机目标的ISAR成像算法研究

逆合成孔径雷达(ISAR)成像技术因其能够提供目标的高分辨率二维图像,已经成为军事侦察、地球观测和灾害监测等领域的重要工具,但是,由于目标的复杂运动,传统的ISAR成像方法往往会出现图像模糊和失真的问题。距离多普勒(RD)算法通过对雷达回波数据进行二维傅里叶变换,可以有效地抑制目标的复杂运动对ISAR图像的影响,从而获得更清晰、更精确的图像,然而,在处理目标加速度和微动方面存在局限性。文中首先说明了ISAR的成像原理,介绍了运动补偿中的包络对齐,在此基础上,提出一种改进的包络对齐方法,通过某卫星成像对该算法进行验证,证明了该方法的适用性和灵活性,运用改进的包络对齐方法对某型舰载机成像,发现该方法可以有效地提高ISAR图像的质量和分辨率。

天基ISAR空间目标的去斜回波相参性恢复方法及其基于FPGA的快速实现技术

逆合成孔径雷达(ISAR)具备二维高分辨成像能力,可实现对空间目标的高精度探测和监视。相比于地基ISAR,天基ISAR能够对空间目标进行更灵活、更大范围的探测成像,为实现空间态势感知提供有利支撑。去斜接收回波体制可以大幅降低采样率,减轻系统硬件负担,现有ISAR系统通常采用此种方式。然而在观测时间内空间目标相对于星载ISAR的相对斜距变化大,雷达需要不断改变录取波门以保证对空间高速目标的稳定跟踪,这严重破坏了雷达回波的相参性。同时,星载ISAR相对空间目标的复杂运动几何导致运动补偿精度下降。针对空间目标ISAR成像问题,文中提出了一种去斜回波相参性恢复方法,此方法可以逐脉冲处理回波,能有效恢复不同距离波门的回波相参性,并利用估计的参数对回波进行精确的运动补偿。基于此方法,设计了一种基于现场可编程逻辑门阵列的去斜接收相参性恢复快速实现方案。仿真实验验证了所提方法及其现场可编程逻辑门阵列实现方案的有效性。

基于ISAR成像和散射中心提取的目标RCS近场测量方法

本研究通过结合逆合成孔径雷达(ISAR)成像和散射中心提取技术,实现了近场测试距离下目标雷达散射截面积(RCS)的有效测量。利用近场球面波修正技术和探头补偿技术减少了近场球面波效应和收发探头对成像质量的影响。对ISAR目标图像采用散射中心提取技术,消除了ISAR目标图像中由于带宽有限所导致的旁瓣效应。文中针对散射中心提取中截断阈值的选取准则进行了深入研究,给出了截断阈值判定的经验公式并结合仿真实验进行了验证。最终利用标准球锥散射体的暗室近场测量数据对文中所提出的方法进行了实测验证,并与多层快速多极子(MLFMA)方法仿真结果吻合良好,验证了文中方法的有效性。

结合最近邻图模型的稀疏ISAR成像方法

逆合成孔径雷达(Inverse Synthetic Aperture Radar,ISAR)稀疏成像方法可提供图像对比度高、旁瓣干扰少的成像结果 .稀疏成像以场景或目标散射率分布具有稀疏性为前提,待成像目标场景的稀疏特性决定了最终成像质量. ISAR目标场景的自然稀疏特性着重刻画点状特征,变换域稀疏表示可增强目标图像的纹理等通用特征.通过学习获得的稀疏变换字典,可自适应于待成像的ISAR目标场景,找到面向ISAR目标图像块的特有稀疏表示.但是,图像块的特有稀疏表示中忽略了待成像目标场景中目标的几何特征信息.最近邻图模型可建立给定数据的几何特征描述算子,刻画出给定数据的几何特征信息.本文利用最近邻图模型来刻画待成像目标场景中目标的几何特征信息,并映射到待成像目标场景的特有稀疏表示中;提出结合最近邻图模型的ISAR稀疏成像方法,用于不同类别实测ISAR数据成像.相比已有的ISAR稀疏成像方法,所提成像方法可获得目标轮廓更清晰的成像结果,成像所需时间平均减少10.4%.

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.