ISAR成像中PFA算法在MTRC中的应用

逆合成孔径雷达(ISAR)转台成像中散射点越距离单元徙动(MTRC)时有发生.在小目标远场情况下,极坐标格式算法(PFA)能够基本消除MTRC,这种算法需要在空间频域进行从极坐标分布到直角坐标分布的二维插值.文中就如何运用PFA算法解决MTRC问题进行了较深入地分析,在距离向给出滤波插值法,并提出了新的补域插值法,方位向则运用斜线投影方法,最终将扇形区域转换为矩形区域.仿真结果表明,此算法比线性距离-多普勒(R-D)算法能更有效的处理MTRC现象;在距离向插值中将滤波法与补域法做了比较,后者计算速度更快.     

基于R-D算法的合成孔径声纳插值方法比较

合成孔径声纳（SAS）中由于基阵相对目标的运动导致回波空间产生了距离徙动，距离徙动的出现将会较大地影响最终声纳的成像质量，目前主要采用插值的方法进行矫正。该文基于SAS中的距离一多普勒（R—D）算法，讨论了距离徙动的成因及矫正思想，详细介绍了几种用于徙动矫正的经典插值方法，并给出了其在SAS成像中的应用表达式，对其性能进行了比较。最后对点目标进行了仿真成像，给出了矫正前后的目标图像，并对不同插值方法的成像质量及运算量进行了分析比较。分析结果表明，这几种插值方法都改善了成像的质量，但拉格朗日法和香农法在改善图像方位向性能上更有优势，可在实际中应用。     

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。     

星载SAR影像上船舶方位向模糊去除算法

目的 掌握海上船舶分布状态对于海上交通流分析和通航安全管理具有重要作用。遥感技术,特别是星载合成孔径雷达(SAR)技术的发展,为大范围海上船舶检测提供了有效的手段,但受SAR成像机制影响,海上船舶目标在星载SAR影像上通常存在着不同程度的方位向模糊噪声,这些噪声易被误判为船舶,导致船舶识别中虚警率提高。方法 本文简述了方位向模糊噪声的产生原因,提出了一种新的星载SAR影像上船舶方位向模糊去除算法,该算法的核心是构建目标方位向角度一致性、方位向位置偏移距离和方位向模糊能量衰减3个判别规则,对潜在SAR影像亮斑目标进行逐层筛选,实现船舶真实目标和方位向模糊目标的判别。结果 选取中国渤海海域和黄海海域的30 m分辨率的Radarsat-2数据进行案例分析,并与船舶自动识别系统(AIS)实测数据进行比对校验,结果表明,传统的双参数恒虚警率(CFAR)算法和基于K分布的CFAR等算法对于船舶难以剔除方位向模糊,容易造成虚警,而本文算法对实验影像的船舶方位向模糊去除准确率优于95.8%,能够有效剔除船舶方位向模糊。结论 该算法为星载SAR影像上船舶方位向模糊去除提供了新的手段,有助于提高SAR影像上船舶目标检测的准确性。     

前斜视SAR成像目标高精度模拟技术

前斜视合成孔径雷达(SAR)成像的距离-方位存在强耦合,目标径向运动信息的模拟精度不仅影响雷达的距离像分辨性能,同时影响方位像分辨性能。通过对距离调制分辨率与SAR成像结果的理论分析,提出了一种频域插值升采样调制的SAR成像回波模拟方法,通过提高采样率以减小距离徙动引起的幅度误差,同时采用GPU并行处理,提高回波模拟的实时性。结果表明:所提出的方法能够达到0.1 m的距离延时模拟精度,并对256×256点场景回波的运算周期小于10μs,可适用于前斜视SAR目标高精度模拟。     

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。     

机载SAR实时成像处理器方位向处理及DSP实现

介绍了基于距离-多普勒算法的成像处理器方位向处理,并针对机载合成孔径雷达(SAR)实时成像探讨了采用DSP并行计算技术实现方位压缩的原理和结果.     

双站聚束SAR极坐标格式成像算法

双站聚束SAR(Synthetic aperture radar)相对于双站条带SAR可以达到更高的方位分辨率,其成像算法受到越来越多的关注.本文介绍了双站聚束SAR成像几何关系,建立了回波信号模型,从Dechirp后的回波数据域出发,提出了一种适合双站聚束SAR的极坐标格式成像算法.该PFA算法采用斜视坐标旋转及沿视线方向的极坐标插值LOSPI,更好地利用空间域数据,适用于正侧视和斜视模式成像.最后分析了距离弯曲对成像区域的限制,以及残余视频相位误差对成像性能的影响,并将此算法与不进行Dechirp的PFA算法相比较.实验结果表明该方法降低了数据率和运算量,算法有效、可靠.     

一种精确的多接收阵合成孔径声纳距离多普勒成像算法

针对现有多接收阵合成孔径声纳算法没有考虑或低估了"停走停"近似在宽测绘带和高平台运动速度情况下带来的图像失真的现象,本文在深入分析多接收阵系统误差产生机理的基础上,提出了一种适用于宽测绘带成像的精确多接收阵合成孔径声纳距离多普勒成像算法,它舍弃了目前普遍采用的等效相位中心(DPC)近似,采用多接收子阵的精确延迟模型.算法首先对各接收子阵数据单独处理求得方位谱并进行方位向扩展,随后分别进行距离徙动校正和方位向脉压,最后将各通道的方位谱相干叠加,反傅里叶变换后得到了高分辨图像.通过点目标仿真比较,该算法在峰值旁瓣比及目标的定位等方面优于现有算法.     

自适应多尺度分块压缩感知算法

目的 基于小波域的多尺度分块压缩感知重构算法忽略了高频信号在重构过程中的作用,丢失了大量的边缘与细节信息。针对上述问题,提出一种自适应多尺度分块压缩感知算法,不仅合理利用低频信息还充分利用图像的高频信息,在图像细节复杂度提高的情况下保证图像重构质量的提高。方法 首先进行3层小波变换,得到一个低频信号和9个高频信号,分别进行小波逆变换后分成大小相同互不重叠的块,对低频部分采用2维邻块边缘自适应加权滤波的方法进行处理,对高频部分采用纹理自适应分块采样,最后利用平滑投影Landweber（SPL）算法对其进行重构。结果 与已有的分块压缩感知算法、基于边缘和方向的分块压缩感知算法和基于纹理和方向的分块压缩感知算法相比,本文算法在不同的采样率下,性能均有所提升,代表细节信息的高频信号得到充分重建,改进的算法所得到的重建图像具有较高的分辨率,尤其对细节较为丰富的图像进行重建后具有较高的峰值信噪比;2维邻块边缘自适应加权滤波有效的去除了重建图像的块效应,且重建时间平均减少了0.3 s。结论 将三层小波变换后的高频分量作为纹理部分,利用自适应多尺度分块重建出图像的轮廓与边缘;将低频分量直接视为平坦部分,邻块边缘自适应加权滤波重建出图像细节,不仅充分利用了图像的高低频信息,还减少了平坦块检测过程,使得重建时间有效缩短。经实验验证,本文算法重建图像质量较好,尤其是对复杂图像明显消除了块效应,边缘和纹理细节较清晰。因此主要适用于纹理细节较复杂的人脸图像、建筑图像和遥感图像等。     

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 Scan SAR imaging method for maritime surveillance via Lp regularization

In this article, a novel Scan mode synthetic aperture radar (SAR) imaging method for maritime surveillance is presented. Conventional Scan SAR is generally operated with severe azimuth resolution loss in order to cover a large area. The proposed imaging method changes the way Scan SAR illuminates sub-scenes and presents a new radar illuminating strategy based on ships’ spatial distribution in each sub-scene. To gain ships’ spatial distribution, a scene sensing algorithm based on radar range profiles together with a peak-seeking and clustering algorithm is introduced. After that, a Markov transfer-probability matrix is generated to make sure that radar illuminates each sub-scene randomly under the probability we calculated before. Finally, an imaging algorithm within the L_p (0 < p ≤ 1) regularization framework is utilized to reconstruct each sub-scene; the regularization problem is solved by an improved iterative thresholding algorithm. The whole wide swath image is joined by putting all the sub-scenes together. Experimental results support that the proposed imaging method can perform high-resolution wide swath SAR imaging effectively and efficiently without reducing the image resolution.     

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 l ₁ 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 l ₁ 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 l ₁ norm based method.     

Three-dimensional space-borne synthetic aperture radar (SAR) imaging with multiple pass processing

Three-dimensional (3D) synthetic aperture radar (SAR) imaging via multiple-pass processing is an extension of interferometric SAR imaging. It exploits more than two flight passes to achieve a desired resolution in elevation. In this paper, a novel approach is developed to reconstruct a 3D space-borne SAR image with multiple-pass processing. It involves image registration, phase correction and elevational imaging. An image model matching is developed for multiple image registration, an eigenvector method is proposed for the phase correction and the elevational imaging is conducted using a Fourier transform or a super-resolution method for enhancement of elevational resolution. 3D SAR images are obtained by processing simulated data and real data from the first European Remote Sensing satellite (ERS-1) with the proposed approaches.     

合成孔径与实孔径雷达谱域成像算法对比分析

讨论了合成孔径雷达(Synthetic aperture radar,SAR)和实孔径雷达(Real aperture radar,RAR)一维扫描方式下的谱域成像实现问题.文中从SAR和RAR扫描下的波数域波散关系入手,分析了这两种扫描方式下的谱域填充区域和成像分辨率,指出了二者的异同,导出了相应的成像算法.单目标和组合目标的雷达成像仿真实验验证了两种扫描方式下成像算法的有效性和理论分析结果.     

Super-resolution imaging based on the BeiDou B3 signal

ABSTRACT

Using global navigation satellites to construct bi-static synthetic aperture radar for imaging has been a major research hotspot in passive radar. However, the low range resolution of Global Navigation Satellite signal (GNSS) limits the quality of actual scene imaging. To increase the range resolution of the imaging, a super-resolution imaging method by mixing the back-projection (BP) algorithm with truncated singular value decomposition (TSVD) is proposed. This paper first introduces the BeiDou Navigation Satellite System (BDS) signal model for ground imaging, carries out the range compression and describes the BP algorithm. Subsequently, the super-resolution method is given and some simulation results are demonstrated. Two field experimental cases, including targets of trees and ferries, are then carried out. The experimental results demonstrate the effectiveness of the proposed method.     

Focusing one-stationary bistatic forward-looking synthetic aperture radar based on squint minimization and modified nonlinear chirp scaling algorithm

ABSTRACT

This article deals with the imaging problem of bistatic forward-looking synthetic aperture radar with a stationary transmitter (one-stationary BFSAR). In one-stationary BFSAR, due to the large forward-looking (or squint) angle, it causes more serious range cell migration and two-dimensional spatial variation. To solve this problem, an imaging algorithm based on squint minimization and modified nonlinear chirp scaling (NLCS) method is proposed. The squint minimization method can decrease the coupling between range and azimuth and correct the linear range cell migration. The modified NLCS can eliminate the variation of azimuth reference function more accurately than traditional NLCS algorithm, which improves the focus quality of marginal targets. Finally, simulation results confirm the efficiency of our proposed algorithm.     

Structured Bayesian compressive sensing with spatial location dependence via variational Bayesian inference

In this paper, a novel non-parametric Bayesian compressive sensing algorithm is proposed to enhance reconstruction performance of sparse entries with a continuous structure by exploiting the location dependence of entries. An approach is proposed which involves the logistic model and location-dependent Gaussian kernel. The variational Bayesian inference scheme is used to perform the posterior distributions and acquire an approximately analytical solution. Compared to the conventional clustered based methods, which only exploit the information of neighboring pixels, the proposed approach takes the relationship between the pixels of the entire image into account to enable the utilization of the underlying sparse signal structure. It significantly reduces the required number of observations for sparse reconstruction. Both real-valued signal applications, including one-dimension signal and two-dimension image, and complex-valued signal applications, including single-snapshot direction-of-arrival (DOA) estimation of distributed sources and inverse synthetic aperture radar (ISAR) imaging with a limited number of pluses, demonstrate the superiority of the proposed algorithm.