基于稀疏线性调频步进信号的ISAR成像

线性调频步进信号在简化雷达系统设计的同时,也存在对高速运动目标易出现Doppler模糊的问题,因此研究如何提高其等效的重复频率具有重要意义.由于ISAR目标的后向散射场具有较强的稀疏性,即大部分能量仅由少数散射中心贡献,所以本文基于稀疏信号表示理论,通过对目标回波模型的分析,提出了一种基于稀疏步进频率信号的逆合成孔径雷达成像方法.该方法通过随机地选择线性调频步进信号的部分子脉冲进行发射,然后使用稀疏信号分解的方法对目标图像进行重建以得到目标的二维高分辨图像.该方法以计算资源为代价,能够有效地去除方位Doppler模糊,同时还能够压低旁瓣并得到超分辨的图像.仿真和实测数据处理结果验证了本文方法的有效性.     

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.     

轨道飞行目标的雷达回波模拟及成像

逆综合孔径雷达（ＩＳＡＲ）是国内９０年代初新兴的一个课题，近几年对空中目标成像的取得了很大的进展，本文通过计算目标后向散射场来模拟轨道飞行目标的ＩＳＡＲ回波，讨论模拟和成像过程并给出模拟成像结果，同时分析了轨道飞行目标成像对雷达重复频率和相干积累时间的要求。     

雷达目标三维特征的提取与识别研究

以逆合成孔径雷达(ISAR)成像技术和计算机视觉理论为基础，提出了一套新的从动态目标ISAR成像序列中提取目标散射点三维结构信息，以此作为目标特征的识别方法。这一研究方法包含了4个重要环节：动态目标的ISAR像序列的获得；“散射点像元”质心的检测、跟踪和匹配；基于光流分析的目标散射点三维结构特征的提取；目标三维特征的识别。由于经过了时间和空间上信息的积累，目标的散射点的三维结构特征具有稳定和直观的特点。作为一种新的目标识别的依据是很有效的，且只需要较少的训练样本就可以获得较高的识别率。     

一种低信噪比下稳健的ISAR平动补偿方法

远距离小目标/隐身目标逆合成孔径雷达成像在目标检测系统中具有重要作用,然而由于回波信号存在信噪比（Signal-to-Noise Ratio,SNR）低等问题,使得传统基于距离包络对齐的平动补偿方法失效;导致后续初相校正和方位聚焦处理性能急剧下降.针对该问题,本文提出一种低信噪比下参数化平动补偿方法.首先,将回波信号建模为多成分的多项式相位信号,并通过挖掘运动目标上所有散射体具有相同的平动历程这一先验信息,利用相位差分（Phase Differencing,PD）和Keystone变换将目标上所有散射体能量聚集到同一距离单元.然后,通过相干积累三次相位函数将该距离单元内的所有散射体能量集中到一个强点,进而估计出目标平动参数实现低SNR下平动补偿.最后,理论分析和实测数据实验验证了该方法的有效性.     

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.     

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.     

Coherent processing for ISAR imaging with sparse apertures

To implement target detection,tracking and imaging in a multifunctional radar system,the wideband measurements for inverse synthetic aperture radar(ISAR)imaging are usually sparsely recorded.Considering the incoherence problem in such sparse-aperture ISAR(SA-ISAR)systems,we concentrate on the study of a coherent processing method in this work.Based on an all-pole model,the incoherence parameters between abutting sub-apertures can be effectively estimated.After coherence compensation,an optimization-based SAISAR imaging approach is provided from the view of statistics.Simulation and real data experiments validate the feasibility and effectiveness of the proposals.     

海面舰船目标ISAR最优成像时间选择算法

通过分析高海情下海上舰船目标的三维摇摆对成像的影响,基于方差定义的 平均多普勒展宽概念,提出了一种逆合成孔径雷达(ISAR)最优成像时间选择算法。该算法根 据目标距离向先验信息估计出舰船的固有纵摇周期,利用该周期隔离微多普勒效应的干扰, 并为多普勒展宽曲线的平滑提供了可靠依据,使得算法整个过程无需人工干预。该算法易实 现且运算量较低,适用于实时应用。仿真和实测数据的处理结果表明,利用本文算法筛选出 的数据段获得了较理想的成像结果。     

基于雷达复杂1维距离数据的3维重建算法

基于刚体目标3维运动过程中的几何不变性,可以利用目标上多个散射点在单天线雷达1维距离像序列中的1维距离数据,重建出目标未知的3维结构和运动路径。针对此1维到3维的几何重构问题,提出了一种雷达刚体目标结构和运动的3维重建算法,该算法可利用散射点复杂的1维距离数据进行重建,并且采用非线性优化技术实现了对目标重建参数的捆绑调整(bundle adjustment)。另外,该算法中引入了目标的平移模型,使目标的平移参数能够与旋转参数一并求解,从而避免了距离对准操作的误差对重建精度的影响。仿真实验结果表明,由于重建数据中散射点的数量以及目标3维运动的丰富性得到了显著提高,尤其是最优化技术在算法中的成功应用,重建算法的鲁棒性得到了有效增强。