基于去调频技术的斜视聚束SAR成像方法

针对具有方位谱混叠现象的斜视聚束式合成孔径雷达(SAR)成像问题,本文研究了一种基于去调频技术的成像方法.文中首先分析了斜视聚束SAR中的方位谱混叠现象,从理论上找出了斜视角影响方位粗聚焦的根本原因.基于分析结果,引入了回波信号非线性相位校正预处理法,彻底消除了斜视角的影响,从而得到正确的粗聚焦结果.然后基于斜视成像模型,推导了预处理回波信号的方位粗聚焦和精聚焦处理过程.在精聚焦的方位滤波处理中,针对非线性相位校正预处理导致的场景聚焦深度限制问题,提出了一种改进的非线性调频变标(NLCS)算法,实现了回波信号的高精度方位压缩.仿真实验结果证明了文中理论分析的正确性和所提方法的有效性.     

斜视SAR分数阶距离多普勒成像算法

针对传统距离多普勒(RD)算法在斜视合成孔径雷达(SAR)成像时运算量不足和边缘存在干扰的问题,将传统的RD算法中的匹配滤波用分数阶Fourier变换替代,提出基于分数阶Fourier变换SAR斜视距离多普勒成像(FrRD)算法。理论分析斜视SAR回波信号分数阶Fourier变换域模型,徙动校正在距离分数阶Fourier域方位频域完成,给出FrRD算法仿真流程。仿真结果表明,与传统RD算法相比,FrRD算法成像副瓣更低,成像边界更加清晰,成像时间缩短近一半。     

基于切比雪夫多项式的双基地前视SAR成像算法

双基地前视合成孔径雷达(SAR)的特殊构型使其距离历程具有双根号和大斜视的特点,给回波信号的成像处理带来困难,也对成像算法的精度提出更高要求,为此提出一种基于切比雪夫多项式的双基地前视SAR RD成像算法.首先,建立双基地前视SAR的几何模型与回波信号模型,指出收发分离构型对二维频谱公式推导过程中驻定相位点求解的影响;然后,引入切比雪夫多项式展开代替传统成像算法中的泰勒级数展开,在此基础上进行双基地前视SAR斜距等效模型和二维频谱模型的推导;最后,通过分析确定双基地前视SAR RD成像算法的相位匹配函数,给出成像算法的完整流程.仿真结果表明,所提出算法可极大减小双基地前视SAR的斜距等效误差与频谱展开误差,有效提高成像算法的聚焦深度和成像质量.     

大斜视双基地SAR的二维可分离成像算法

提出了大斜视双基地SAR的二维可分离成像算法.首先建立了大斜视双基地SAR回波信号模型,并对其距离徙动和方位调频率的变化特性进行了分析;在此基础上,将二维可分离成像算法推广应用到了双基地SAR领域,解决了大斜视双基地SAR回波信号线性距离徙动补偿后方位调频率在方位向出现的空变性问题.算法采用了由平移不变双基地SAR几何关系所确定的新匹配函数,并考虑了3次相位项对成像效果的影响,改善了成像的效果.最后通过点目标仿真验证了算法的有效性.     

An exact wide field digital imaging algorithm

Abstract

A new imaging algorithm is presented for Synthetic Aperture Radar (SAR) that is exact in the sense that it is capable of producing a complex image with excellent geometrical, radiometrical and phase fidelity. No interpolations or significant approximations are required, yet the method accomplishes range curvature correction over the complete range swath. The key to the approach is a quadratic phase perturbation of the range linearly frequency modulated signals while in the range signal, azimuth frequency transform (Doppler) domain. Range curvature correction is completed by a phase multiply in the two-dimensional frequency domain. Other operations required are relatively conventional. The method is generalizable to imaging geometries encountered in squint and spotlight SAR, inverse SAR, seismics, sonar, and tomography.     

基于OpenMP的高效多子阵合成孔径声呐距离多普勒成像算法

为充分利用多核CPU计算资源解决多子阵合成孔径声纳成像效率低的问题,提出了一种共享内存环境下的距离多普勒成像算法并行解决方案。在分析多子阵合成孔径声纳距离多普勒成像算法并行性的基础上,对算法中预处理、距离向脉冲压缩、固定相位补偿、距离徙动校正和方位向脉冲压缩进行了OpenMP并行化设计,充分利用多核CPU计算资源实现了大数据量合成孔径声纳图像快速重构。对实测数据的成像实验结果表明,并行成像算法加速比高达19.86,满足实时合成孔径声纳系统成像需求。     

一种斜视滑动聚束SAR子孔径处理成像方法

子孔径法是解决滑动聚束SAR成像方位频谱混叠的有效方法,具有内存占用量低、灵活性高的特点。子孔径的划分不仅影响成像 质量,还会影响成像效率;但目前子孔径的划分大多采用经验值,理论上并没有给出明确分析。因此在斜视成像过程中,未合理划分的子孔径会导致拼接谱出现“空隙”现象。本文首先介绍了斜视滑动聚束SAR工作模型,分析了其方位多普勒历程,提出了斜视下基于方位频域Scaling的子孔径成像算法。根据相邻子孔径的二维频谱,研究了子孔径频谱拼接的准则,推导了确定子孔径长度与重叠率的解析式,给出了子孔径划分的流程,最后通过仿真 实验和实测数据处理验证了子孔径划分方法及斜视下成像算法的有效性。     

基于ISAR成像的目标大角度RCS外推

传统逆合成孔径雷达(ISAR)转台成像算法对目标进行成像后只能外推小角度区域雷达散射截面(RCS)。针对这一问题,建立了大转角转台成像系统,提出了一种基于近场微波成像的RCS外推算法,分析了近场大转角转动带来的越距离单元徙动问题。通过将距离与方位域解耦,并对方位方向进行圆周卷积运算,得到高质量的目标成像,进而可以外推目标360度方位的RCS,并与传统ISAR小角度成像算法进行比对。仿真结果表明,外推算法不仅可以对目标进行成像,诊断目标的强散射点位置,还可以用来做远区RCS大角度外推,并结合成像结果分析目标散射机理。     

异构环境下的多子阵合成孔径声呐距离多普勒成像算法

为解决多子阵合成孔径声纳成像效率低的问题,提出了一种异构环境下的多子阵合成孔径声纳快速成像方法。根据多子阵合成孔径声纳距离多普勒成像算法特点以及CPU和GPU各自计算特点,通过将算法中距离向脉冲压缩、固定相位补偿、距离徙动校正和方位向脉冲压缩密集型运算采用GPU计算,极大提高了多子阵合成孔径声纳成像效率。最后通过实测数据的成像实验对所提算法的正确性和高效性进行了验证,与串行计算方法相比加速比高达14.45。     

基于变结构控制的固定前置角制导律设计

弹载合成孔径雷达（SAR）的方位成像能力受飞行导弹的观测角影响,其末制导段采用前侧视工作方式。因此,必需合理设计制导律使前侧视条件成立。为了保证末制导段弹目间具有合适的夹角,采用变结构控制方法设计了一种固定前置角制导律,该制导律能够解决合成孔径雷达导引头对于前置角约束的问题。在制导律设计过程中,首先建立了弹目相对运动关系模型和具有终端角度约束时的视线角变化模型;在此基础上采用变结构控制的方法设计了固定前置角制导律;进而,对该固定前置角导引律进行了性能分析,得出了其一般攻击特性;最后,通过弹道仿真论证了其正确性与有效性。     

基于距离徙动轨迹的空间目标ISAR联合运动补偿算法

针对空间高速运动目标的运动特征,分析目标距离徙动轨迹（Range migration trajectory,RMT）与等效运动模型,提出了一种基于距离徙动轨迹的联合运动补偿算法。该算法依据距离像全局熵值最小化原则,从RMT中估计出目标的平动参数,根据平动参数分别补偿距离像偏移并校正一维距离像畸变,从而实现对空间目标回波的距离对齐和脉内走动的联合平动补偿。仿真和实测数据处理结果表明该算法准确性较高,更重要的是,距离对齐步骤不会引入随机偏移误差和相位误差,这也是应用高分辨成像方法的前提条件。     

带距离徙动校正的压缩感知PFA成像方法

目的 极坐标格式算法（PFA）是合成孔径雷达（SAR）聚束模式下的一种高分辨率成像算法,方位向增加孔径长度带来了数据存储和传输的负担,利用压缩感知进行合成孔径雷达成像可以减小采样率,以前的研究往往认为图像是2维可分离的而忽略距离徙动的影响,造成了图像质量的下降。提出一种在方位向利用压缩感知处理的PFA成像算法,可以校正距离徙动,保证压缩感知成像的图像分辨率。方法 在方位向进行压缩感知处理的过程时,采用了随距离空间频率变化的傅里叶基。结果 该方法可以有效代替PFA处理过程中的方位向插值,消除距离徙动的影响,保证距离向和方位向的分辨率。结论 仿真和实测数据的处理结果证明了该方法的有效性。     

SRTM DEM enhancement using a single set of PolSAR data based on the polarimetry-clinometry model

ABSTRACT

The freely available global and near-global digital elevation models (DEMs) have shown great potential for various remote sensing applications. The Shuttle Radar Topography Mission (SRTM) data sets provide the near-global DEM of the Earth’s surface obtained using the interferometry synthetic aperture radar (InSAR). Although free accessibility and generality are the advantages of these data sets, many applications require more detailed and accurate DEMs. In this paper, we proposed a modified and advanced polarimetry-clinometry algorithm for improving SRTM topography model which requires only one set of polarimetric synthetic aperture radar (PolSAR) data. The azimuth and range slope components estimation based on polarization orientation angle (POA) shifts and the intensity-based Lambertian model formed the bases of the proposed method. This method initially compensated for the polarimetry topography effect corresponding to SRTM using the DEM-derived POA. In the second step, using a modified algorithm, POA was obtained from the compensated PolSAR data. The POA shifts by the azimuth and range slopes’ variations based on the polarimetric model. In addition to the polarimetric model, a clinometry model based on the Lambertian scattering model related to the terrain slope was employed. Next, two unknown parameters, i.e. azimuth and range slope values, were estimated in a system of equations by two models from the compensated PolSAR data. Azimuth and range slopes of SRTM were enhanced by PolSAR-derived slopes. Finally, a weighted least-square grid adjustment (WLSG) method was proposed to integrate the enhanced slopes’ map and estimate enhanced heights. The National Aeronautics and Space Administration Jet Propulsion Laboratory (NASA JPL) AIRSAR was utilized to illustrate the potential of the proposed method in SRTM enhancement. Also, the InSAR DEM was employed for evaluation experiments. Results showed that the accuracy of SRTM DEM is improved up to 2.91 m in comparison with InSAR DEM.     

基于Legendre多项式展开的双基SAR二维频域成像算法

针对任意构型双基SAR基于传统Taylor级数展开斜距方法的边缘点散焦问题,提出了一种基于Legendre正交多项式逼近的双基SAR二维频域成像算法。该算法基于对斜距函数的Legendre多项式展开推导了点目标二维频谱,解除了回波信号距离—方位的耦合,使成像处理更高效。在二维频域对场景区域的点目标采用二维频域成像算法进行成像。该算法改善了场景边缘点的聚焦效果,增加了场景边缘点的聚焦深度。理论推导和仿真结果验证了该算法的有效性和可行性。     

基于四阶传递函数大斜视角SAR成像算法

针对研究SAR大斜视工作时,为增加天线实时侦察效果,对低阶ETF(Exact transfor function)算法处理方位向相位的近似误差进行改进,在传统ETF成像算法基础上研究了四阶传递函数算法.采用四阶距离模型,分析了距离向聚焦深度为分块数据处理提供了依据,同时在距离多普勒域对非参考距离处目标进行了相位补偿.通过场景目标仿真对算法有效性进行了验证.仿真结果表明,算法可有效补偿大斜视角时方位向近似误差,提高了精度,满足大斜视时成像要求,同时避免插值处理,计算量小.便于实时成像.     

基于改进FS算法的反舰导弹导引头成像研究

针对开发巡航式反舰导弹雷达导引头成像功能这一问题,设计了导弹的典型飞行轨迹,并采用改进的聚束式SAR—FS成像算法在导弹飞行过程中对舰船目标进行成像。导弹飞行过程中,斜视角随之变化,在接近目标时逐渐增大,改进的FS算法引入了SRC的非线性变标对数据进行二次距离压缩,有效地减少了距离压缩误差,以达到较好的聚焦效果,提高成像质量。计算机仿真结果表明,在末端成像制导阶段,当斜视角达到以上时,改进的FS算法也能达到较好的成像效果,为完成攻击点的选择,提高角跟踪精度创造了条件。     

Bregmanized Domain Decomposition for Image Restoration

Computational problems of large-scale data are gaining attention recently due to better hardware and hence, higher dimensionality of images and data sets acquired in applications. In the last couple of years non-smooth minimization problems such as total variation minimization became increasingly important for the solution of these tasks. While being favorable due to the improved enhancement of images compared to smooth imaging approaches, non-smooth minimization problems typically scale badly with the dimension of the data. Hence, for large imaging problems solved by total variation minimization domain decomposition algorithms have been proposed, aiming to split one large problem into N>1 smaller problems which can be solved on parallel CPUs. The N subproblems constitute constrained minimization problems, where the constraint enforces the support of the minimizer to be the respective subdomain. In this paper we discuss a fast computational algorithm to solve domain decomposition for total variation minimization. In particular, we accelerate the computation of the subproblems by nested Bregman iterations. We propose a Bregmanized Operator Splitting–Split Bregman (BOS-SB) algorithm, which enforces the restriction onto the respective subdomain by a Bregman iteration that is subsequently solved by a Split Bregman strategy. The computational performance of this new approach is discussed for its application to image inpainting and image deblurring. It turns out that the proposed new solution technique is up to three times faster than the iterative algorithm currently used in domain decomposition methods for total variation minimization.     

A new generic processing scheme for large baseline spaceborne bistatic SAR data imaging

Standard synthetic aperture radar (SAR) processors use analytically derived transfer functions that rely on the assumption of hyperbolic range histories of monostatic SARs with straight flight paths. The range histories for bistatic SARs are no longer hyperbolic and simple analytic transforms do not exist. In this article, a bistatic subsection hyperbolic range history (BS-HRH) model is proposed and based on it; a generic processing scheme for bistatic SAR imaging is given with which simple analytic transforms for standard monostatic SARs can still be applied. One-dimensional and two-dimensional signal-level simulations are done using the orbit parameters of Envisat and a classic pendulum-like bistatic configuration under different baselines. It is found that the proposed BS-HRH algorithm performs well for spaceborne bistatic SARs even with extreme bistatic configurations and/or very high azimuth resolution. Compared with the existing analytical ‘method of series reversion (MSR)’ and the numerical ‘NuSAR’ algorithm, easy implementation, high efficiency and accuracy, and mathematical simplicity are among the advantages of the proposed algorithm. The approach of BS-HRH has the potential to solve the general HRH-deviation problem for other new SARs with more complex imaging geometry.     

High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.     

A novel spaceborne SAR wide-swath imaging approach based on Poisson disk-like nonuniform sampling and compressive sensing

Since the range swath width in the conventional single channel spaceborne synthetic aperture radar(SAR)is restricted by the system parameters,there is a trade-off between the azimuth resolution and the swath width in order to satisfy the Nyquist sampling criterion.In this paper,we propose a novel spaceborne SAR wide-swath imaging scheme based on compressive sensing(CS)for the sparse scene.The proposed method designs a Poisson disk-like nonuniform sampling pattern in the azimuth direction,which meets the demand of wider swath by restricting the smallest time interval between any two azimuth samples,with the conventional sampling pattern preserved in the range direction.By a similar way to the processing procedure of spectral analysis(SPECAN)algorithm,the linear range migration correction(RMC)is realized while carrying out range compression,which can meet the demand for focusing with middle level resolution.To reduce the computation load of CS reconstruction,we propose a novel fast reconstruction algorithm based on nonuniform fast Fourier transform(NUFFT),which greatly reduces the computation complexity from O(2M N)to O(4N log N).Experiment results validate the effectiveness of the proposed methods via the point target simulation and the Radarsat-1 raw data processing in F2 mode.