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.     

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.     

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。     

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。     

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。     

一种高机动目标的宽带信号检测、参数估计及成像方法

在低信噪比情况下,需要通过方位相干积累来提高信噪比,对目标进行有效的检测.但当目标进行高机动飞行时,其产生的径向速度、加速度及加加速度会造成严重的包络徙动和高次方位相位项,影响方位聚焦的质量,降低信噪比,不能有效的对目标进行检测和成像.针对这些问题,本文提出了一种基于相邻相关和尺度变换法的高机动目标宽带信号检测、参数估计及成像方法.该方法能有效地校正包络徙动并消除目标径向运动所造成的方位高次相位项的影响,对目标进行检测和参数估计.在本文的最后,通过宽带信号的仿真数据验证了该方法能在低信噪比情况下,对高机动飞行的目标进行检测、参数估计及成像.     

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。     

弹载SAR俯冲段斜视成像算法

弹载SAR在俯冲段具有较大的下降速度和加速度,这使得距离徙动校正(Range cell migration correction,RCMC)变得困难,并且由于导弹在攻击目标前需要有一定的转弯机动时间,弹载SAR还往往工作在斜视模式下,此时常规的SAR成像算法不再适用.本文提出了一种弹载SAR俯冲段斜视成像算法,该方法在建立弹载SAR回波信号模型的基础上,首先在距离频域-方位时间域校正距离走动的主要部分,然后结合级数反演思想,推导出SAR回波信号的二维频域表达式,在距离频域-方位多普勒域进行剩余的距离走动和距离弯曲的校正,最后完成二维脉冲压缩,得到聚焦的图像.运算量的定量分析和实验仿真的结果验证了该算法的可行性和有效性.     

间接同步连续采样模式双基地ISAR时间同步仿真

研究了间接同步连续采样模式下双基地ISAR的时间同步问题.首先分析了双基地ISAR时间系统的特点,建立了间接同步连续采样模式双基地ISAR的时间模型,将其表示为由快时间、慢时间、时间同步误差描述的时间结构.基于该模型,分析了连续采样对双基地ISAR成像的影响,研究了时钟非理想因素对回波相位及成像的影响,并进行了仿真.仿...     

小转角下空间目标双基地ISAR二维成像算法

为了解决小转角下空间目标双基地ISAR方位向分辨率下降的问题,提出基于AR模型数据外推的双基地ISAR成像算法.双基地ISAR成像时,小转角下的方位向回波可认为服从AR模型,据此建立线性预测方程,利用Burg熵最大法中的Levions递推估计预测系数,然后对方位向回波进行外推,最后基于原始数据和外推后的数据共同进行谱估...     

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.     

ISAR imaging of maneuvering target with complex motions based on ACCF–LVD

This paper considers the inverse synthetic aperture radar (ISAR) imaging problem for a maneuvering target with complex motions, involving range migration (RM) and Doppler frequency migration (DFM) within the coherent integration period of radar imaging, which will degrade the imaging quality. A nonsearching ISAR imaging algorithm based on adjacent cross correlation function (ACCF) and Lv's distribution (LVD), i.e., ACCF–LVD, is proposed, where the ACCF is applied to correct the RM and reduce the order of DFM. Then the motion parameters are estimated via LVD and Fourier transform. With the estimated motion parameters, high quality ISAR images can be achieved. The advantage of the presented method is that it can estimate the motion parameters under low signal-to-noise ratio (SNR) without searching procedures. Finally, several simulation examples are shown to confirm the validity of the proposed algorithm.     

空间轨道目标ISAR成像方法

研究了高速空间目标逆合成孔径雷达(Inverse synthetic aperture radar，ISAR)成像问题；根据空间目标回波模型提出了先进行速度补偿再进行平动补偿的ISAR成像方法。研究了高速运动目标回波模型，针对空间目标回波为线性调频信号的特点，提出采用CLEAN算法的线性调频信号参数估计方法对回波进行速度补偿。最后对自旋和非自旋两类轨道飞行目标成像进行了分析。仿真结果验证了理论分析的正确性和成像方法的有效性。     

基于图像熵的线性阵列ISAR成像处理方法

研究一种线性阵列逆合成孔径雷达(LAAISAR)的成像处理方法。分析线性阵列ISAR的几何模型并给出成像处理流程。针对阵列ISAR同时接收多路回波信号的特点,采用传统的距离-多普勒算法对每路回波进行成像;利用互相关法进行图像粗配准;在此基础上利用图像熵法进行图像细配准,得到匹配的目标图像。基于该处理流程的仿真实验表明,该处理方法能够充分利用阵列ISAR的每路回波信息,使目标图像精确配准,且能有效提高目标分辨率。     

Cross-range scaling for non-uniformly rotating targets by sharpness maximization

Cross-range scaling is a crucial work to target recognition in inverse synthetic aperture radar (ISAR) systems. However, it is still challenging for maneuvering targets due to the time-varying Doppler history, which degrades the image as well. This paper presents a novel method to estimate the scaling factor by optimizing the image quality. One highlight of this work is the usage of Matching Fourier Transform (MFT), which coincides with the non-uniform rotation model or the variant Doppler. The backscattered reflectivity can be concentrated in MFT domain when the parametric transform is perfectly matched with the signal form. Thus another novelty lies in the sharpness maximization of the MFT image by iteratively compensating the range-dependent phase error joint with the image formation. The proposed scheme manages to output the scaling factor and enhanced images. To alleviate the influence of residual translation phase error on estimation, conventional autofocus methods are invoked during the iterations. Simulation and real-data results confirm the effectiveness of the proposed method.     

ISAR imaging of complex motion targets based on Radon transform cubic chirplet decomposition

For targets with complex motion, the echo of inverse synthetic aperture radar (ISAR) is a time-varying frequency signal in azimuth. Hence, the traditional range-Doppler (R-D) algorithm based on a constant frequency is invalid. In this letter, a novel ISAR imaging method for targets with complex motion is presented. The echo in azimuth is characterized as an amplitude-modulated (AM)-cubic phase signal, which is closer to the real ISAR scene, and Radon transform cubic chirplet decomposition (RTCCD) algorithm is proposed to process the signal. By introducing Radon transform and improved cubic chirplet function (CPF), the proposed algorithm estimates the chirp rate and the cubic chirp rate simultaneously to avoid error accumulation. Therefore, the parameter estimation precision is improved, and a high quality ISAR image can be obtained. Simulations and real data experiment validate the effectiveness of the proposed method.     

A novel cognitive ISAR imaging method with random stepped frequency chirp signal

The random stepped frequency chirp signal(RSFCS)has better performance in anti-jamming than that of conventional stepped frequency chirp signal(SFCS).In combination with the theory of compressing sensing(CS),a novel ISAR imaging method is proposed based on RSFCS,in which the high resolution range profile(HRRP)is reconstructed by using the conventional OMP algorithm,whereas the cognitive approach is introduced to further reduce the number of sub-pulse in RSFCS.In the proposed method,via cognizing the characteristics of moving targets,the number of sub-pulse in each burst can be adjusted adaptively.Finally,in the cross-range direction,the accurate reconstruction of ISAR image by using CS theory is implemented,which can effectively accomplish unwrapping.With the proposed method,high quality HRRP and ISAR image can be achieved with fewer sub-pulses of RSFCS and lower burst repetition frequency(BRF).Some simulation results are given to validate the effectiveness and robustness of the proposed algorithm.     

基于Keystone变换和方位向加权的MTRC补偿算法

该文对keystone变换进行了详细的分析,发现了其在处理越距离单元走动（MTRC）问题时的局限性,提出了一种新的MTRC补偿算法,通过实测数据和仿真数据对新方法进行了实验,实验结果表明了新算法的有效性。