太赫兹目标RCS缩比测量技术

论述了国外基于微波上倍频、激光下变频、THz时域光谱三种典型太赫兹波目标雷达散射截面(RCS)缩比测试系统的构成、技术特点和应用范围。采用固态器件、基于微波上倍频技术集成研制了宽带太赫兹低频端目标RCS测量系统,采用基于扫频的时域法RCS测量技术和小角度旋转目标雷达合成孔径（ISAR）成像方法对小目标进行了电磁散射特性测量,获取了目标RCS和二维散射成像信息。     

扩展性微动目标回波模拟与特征参数提取研究

研究了微动目标的多普勒回波模拟及特征参数提取技术. 提出了一种基于物理光学法和等效电磁流法的扩展性微动目标回波模拟方法. 将在目标坐标系下计算得到的后向散射场通过坐标转换, 成为雷达坐标系下的目标回波, 通过与解析信号模型对比验证方法的正确性. 分析了圆锥与带翼弹头的进动特性, 为获得较好的时频聚集性同时避免交叉项采用S-method方法对获取的回波信号进行时频分析, 分析了不同雷达波入射角度, 不同运动状态及不同几何外形的时频分布特点. 对时频分布图进行逆Radon变换, 将正弦曲线映射到参数空间, 从而获取目标的微动参数. 该研究结合电磁散射与信号处理技术, 通过对典型弹道目标的仿真, 获得一些不同于传统微动模型的结果, 结合电磁散射理论, 对这些现象进行了解释分析. 该研究成果在弹道目标的探测识别领域具有重要的理论与应用价值.     

一种逆合成孔径雷达成像干扰方法

逆合成孔径雷达（ISAR）是一种高分辨率的二维成像雷达，必须经过运动补偿才能实现目标的二维成像。运动补偿实质是补偿由目标运动引起的相邻距离像之间的相位。如果通过无源或有源的方法对雷达回波的相位进行调制，必然会干扰ISAR的二维成像。在ISAR的无源干扰方面：有关文献分析了目标的时变抖动运动、随机运动对ISAR成像的影响。有源干扰可以采用噪声干扰和欺骗干扰。本文将脉冲压缩失配法的概念引申到ISAR的有源对抗中，提出了基于正弦调相的ISAR干扰方法。     

火箭尾焰对高频回波的影响及其频域能量凝聚检测方法

针对火箭尾焰喷射出的大量物质消耗大气中电子密度以及干扰高频电磁波传播的问题,本文从分析火箭尾焰高频雷达回波特性入手,重点研究了尾焰电磁特性和对高频电磁波衰减的物理机理,分析了尾焰雷达散射特性与尾焰高频回波产生的时机.在此基础上,提出了一种尾焰回波频域能量凝聚的目标检测方法.通过仿真实验和理论分析,得到了火箭尾焰高频电磁回波与海拔高度、尾焰电磁特性、电波频率、入射角之间的关系,所提检测方法能够及时有效检测火箭目标,并降低虚警和漏警概率.     

非视域成像中的中介面散射特性与成像仿真

中介面与目标表面光学散射特性对成像结果有直接影响,目前各种非视域成像方法多针对朗伯体中介面进行计算成像,系统结构复杂且成本昂贵。然而,应用场景中的常见材料均为非朗伯体,故基于中介面材料的双向反射分布理论,提出了一种材料散射特性描述方法。通过设置中介面中所包含的不同散射成分,经过大量光线追迹,实现了对非视域目标光强信号的追踪与仿真。仿真成像结果采用标准差进行评价,通过多因素方差分析,定量讨论了不同散射成分组合与非视域成像质量之间的关系。分析结果表明,材料中高斯散射角对非视域成像质量有显著影响。     

基于变量解耦的俯冲加速段弹载SAR大场景成像算法

俯冲加速阶段导弹运动状态复杂,成像范围需求大,回波处理困难的特点使得传统的成像算法难以运用.针对这一问题,本文首先建立了精确的斜距几何和回波模型,并在分析二维频谱的基础上,提出了一种基于变量解耦下的非线性变标算法,有效的补偿了随场景纵向和横向的多普勒调频率,改善了方位聚焦质量和位置的正确性,提高成像质量的同时简化了后期图像几何校正操作.多个散射点的仿真数据和结果表明了算法的有效性.     

逆合成孔径成像激光雷达数据采样技术

逆合成孔径成像激光雷达能够实现对运动目标的高分辨实时成像,但激光信号的极大带宽和目标回波信号的微弱性给雷达回波数据的接收和处理带来了较大困难.针对这一问题,提出了基于光外差探测手段和压缩感知理论相结合的信号采样方法,首先通过光外差探测降低回波信号的有效带宽,再结合压缩感知理论实现对信号的稀疏化采样和重构.仿真结果证明了运用本文所提出的采样方法,在使用远低于奈奎斯特定理所规定的采样率时,仍然能够实现对目标的高质量成像.     

逆合成孔径成像激光雷达数据采样技术

逆合成孔径成像激光雷达能够实现对运动目标的高分辨实时成像,但激光信号的极大带宽和目标回波信号的微弱性给雷达回波数据的接收和处理带来了较大困难.针对这一问题,提出了基于光外差探测手段和压缩感知理论相结合的信号采样方法,首先通过光外差探测降低回波信号的有效带宽,再结合压缩感知理论实现对信号的稀疏化采样和重构.仿真结果证明了运用本文所提出的采样方法,在使用远低于奈奎斯特定理所规定的采样率时,仍然能够实现对目标的高质量成像.     

不同表面结构特征圆柱导体的太赫兹散射特性

太赫兹频段下导体表面的细微结构、粗糙度等细节将对目标电磁散射行为产生影响。为衡量这一影响程度,以圆柱导体为例研究了太赫兹频段下目标表面不同结构特征的电磁散射现象及其在图像域的表现规律。利用高频电磁计算方法获得了表面分别为理想光滑、带刻痕和周期粗糙的三种圆柱多姿态角、多频点单站散射场;基于转台成像算法重建了小转角下目标的二维图像。从仿真结果可以看出:m量级的细节特征在太赫兹雷达图像上有着显著的表现,表明太赫兹雷达能够获取更加丰富和精细的目标信息,从而为目标探测识别提供新的特征和技术手段。     

基于压缩感知的窄带高速自旋目标超分辨成像物理机理分析

常规窄带雷达系统对高速自旋的空天目标成像时,方位脉冲重复频率通常难以满足采样率要求.而基于压缩感知(compressive sensing,CS)理论则可实现欠采样条件下窄带高速自旋目标的成像.本文对这一成像的物理机理进行分析和讨论.首先,构建方位欠采样回波模型,分析了该模型与CS理论的关系;其次,从物理角度分析基于CS理论可以保证欠采样条件下散射点准确重构的机理,给出欠采样倍数的理论下限值.仿真结果表明,欠采样条件下窄带雷达系统可实现对高速自旋目标二维成像,同时验证了基于CS的欠采样成像性能与欠采样倍数、等效强散射点个数以及波长等有关,与信号带宽无关等结论.     

双基地角时变下的逆合成孔径雷达越分辨单元徙动校正算法

采用双基地逆合成孔径雷达距离-多普勒算法成像时,目标尺寸较大或累积转角过大,会引起越分辨单元徙动现象,影响成像质量.针对双基地角时变下逆合成孔径雷达成像的越分辨单元徙动问题,提出了一种校正算法.首先,建立了双基地逆合成孔径雷达回波模型,分析了越分辨单元徙动的产生机理,并通过广义的Keystone变换实现了越距离单元徙动的校正,同时消除了目标非匀速转动对成像的影响.然后,基于图像对比度最大准则估计了图像的等效旋转中心位置,并对距离向绝对定标,进而构造补偿相位项,完成了越多普勒单元徙动的校正.仿真实验结果表明,此方法能够有效地校正双基地角时变下的越分辨单元徙动,提高成像质量.     

Photonics-based real-time and high-resolution ISAR imaging of non-cooperative target

Real-time and high-resolution imaging is demonstrated based on field trial detection of a non-cooperative target using a photonics-based inverse synthetic aperture radar(ISAR).By photonic generation and de-chirping of broadband linear frequency modulation signals,the radar can achieve a high range resolution thanks to the large instantaneous bandwidth(8 GHz at the K band),as well as real-time ISAR imaging using low-speed analog-to-digital conversion(25 MSa/s).A small-size unmanned aerial vehicle is employed as the noncooperative target,and ISAR imaging is realized with a resolution far better than those achieved by the previously reported photonics-based ISARs.The capability for real-time ISAR imaging is also verified with an imaging frame rate of 25 fps.These results validate that the photonics-based radar is feasible in practical real-time and highresolution ISAR imaging applications.     

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.     

High resolution inverse synthetic aperture radar imaging of three-axis-stabilized space target by exploiting orbital and sparse priors

The development of inverse synthetic aperture radar(ISAR) imaging techniques is of notable significance for monitoring, tracking and identifying space targets in orbit. Usually, a well-focused ISAR image of a space target can be obtained in a deliberately selected imaging segment in which the target moves with only uniform planar rotation. However, in some imaging segments, the nonlinear range migration through resolution cells(MTRCs) and time-varying Doppler caused by the three-dimensional rotation of the target would degrade the ISAR imaging performance, and it is troublesome to realize accurate motion compensation with conventional methods. Especially in the case of low signal-to-noise ratio(SNR),the estimation of motion parameters is more difficult. In this paper, a novel algorithm for high-resolution ISAR imaging of a space target by using its precise ephemeris and orbital motion model is proposed. The innovative contributions are as follows. 1) The change of a scatterer projection position is described with the spatial-variant angles of imaging plane calculated based on the orbital motion model of the three-axis-stabilized space target. 2) A correction method of MTRC in slant-and cross-range dimensions for arbitrarily imaging segment is proposed. 3) Coarse compensation for translational motion using the precise ephemeris and the fine compensation for residual phase errors by using sparsity-driven autofocus method are introduced to achieve a high-resolution ISAR image. Simulation results confirm the effectiveness of the proposed method.     

Deep learning-based circular disk type radar target detection in complex environment

Target identification is one of the most popular radar uses in real life. Target identification is a classifier that analyzes whether a signal contains an echo from a target (target-present) or is merely noise (target-absent). Deep learning techniques are a popular topic in classification, and they have evinced to be effective in a range of applications. In this paper, a 64 layers Circular Disk type RADAR Target Detection (CDRTD) model is proposed based on Transfer Learning using the SqueezeNet architecture of Convolutional Neural Network (CNN) that functions directly with processed radar target return eco signal and minimize the requirement of conventional laborious radar signal processing. Further, the proposed 64 layers SqueezeNet-based CNN CDRTD model was then implemented to identify circular disk type targets in complex environment. Finally, the target return eco data was tested to identify the circular disk type radar target in complex environments. We further analyzed target detection probability, false alarm rate, precision, recall, F1 in a complex environment and compared it with the ideal case. We found that our proposed CDRTD model can classify 83.3% of the test samples correctly with an overall accuracy of 94.59% in a noisy and cluttered environment whereas 100% of the test samples are classified correctly with an overall accuracy of 100% in an ideal environment.     

0.14 THz高分辨力成像雷达信号处理

为适应0.14 THz超高分辨雷达实时成像的需求,开发了基于CPU+GPU+FPGA的硬件架构和成像处理算法,算法以距离-多普勒为原型,引入L类维格纳分布变换提高横向分辨力,用Keystone变换方法对越距离单元徙动进行校正,并开发了系统非线性补偿算法。在载频0.14 THz、带宽5 GHz雷达样机上进行了逆合成孔径雷达成像试验,获得了3 cm3 cm的成像分辨力和实时成像能力,验证了信号处理方法的有效性。     

基于扩展信源熵值的穿墙成像雷达墙体强杂波抑制

提出了基于扩展信源熵值理论的超宽带穿墙成像雷达墙体强杂波抑制方法. 首先将回波信号离散化, 计算离散信源的概率空间并对该离散信源进行扩展, 计算得到扩展后含有墙体强杂波和目标回波的新信源的熵值. 然后根据墙体杂波熵值与目标信号熵值的差异设定门限, 自适应选取最佳门限调节因子, 对回波信号进行杂波抑制处理. 经过墙体强杂波抑制处理后, 利用后向投影方法对目标进行成像. 以基于时域有限差分方法(Finite Difference-Time Domain, FDTD) 的仿真软件GprMax2D/3D所获得的穿墙雷达数据进行仿真实验, 分别通过基于信源熵值的方法与本文所提方法来抑制墙体强杂波并成像, 通过对比结果可知, 前者的目标-杂波比增量为15.51 dB, 后者的目标-杂波比增量为19.74 dB. 因此, 本文所提方法能够在相同测量方式下得到更为精确的成像, 而且可以在保证成像效果的前提下大大减少天线扫描次数.     

基于卷积神经网络的雷达目标航迹识别研究

现代战争中雷达信号日趋复杂,如何快速准确地从种类繁多、数据量庞大的雷达检测数据中,获取目标航迹的类别信息,为战场指挥提供准确有效的信息是当前急需解决的难题。传统基于人的经验认知的雷达目标航迹识别方法已经无法有效应对瞬息万变的战场和海量数据。根据实际雷达数据特点,提出了使用对数的雷达航迹预处理方法,并构建了基于卷积神经网络的深度学习模型,实现了对雷达对抗中的目标航迹的识别与检测。基于模拟生成的雷达目标航迹数据对提出的数据预处理方法和构建的模型进行测试;实验表明,所提出的方法能很好地实现对目标航迹的检测与识别。     

Influence of the source's energy fluctuation on computational ghost imaging and effective correction approaches

We investigate the influence of the source's energy fluctuation on both computational ghost imaging and computational ghost imaging via sparsity constraint,and if the reconstruction quality will decrease with the increase of the source's energy fluctuation.In order to overcome the problem of image degradation,a correction approach against the source's energy fluctuation is proposed by recording the source's fluctuation with a monitor before modulation and correcting the echo signal or the intensity of computed reference light field with the data recorded by the monitor.Both the numerical simulation and experimental results demonstrate that computational ghost imaging via sparsity constraint can be enhanced by correcting the echo signal or the intensity of computed reference light field,while only correcting the echo signal is valid for computational ghost imaging.     

低信噪比下的二维联合线性布雷格曼迭代快速超分辨成像算法

针对实际逆合成孔径雷达(ISAR)成像时带宽有限、方位孔径稀疏的小角度回波数据条件下,常规算法的成像分辨率不高等问题,基于压缩感知理论,提出了一种低信噪比条件下的二维联合布雷格曼迭代快速ISAR超分辨成像算法.首先,将雷达回波构建为距离频域-方位多普勒域的二维稀疏表示模型,在此基础上,将二维超分辨成像问题转换为二维联合压缩感知的稀疏重构问题;其次,为了避免重构时向量化操作带来的复杂度,提出了二维联合布雷格曼迭代算法,为实现快速重构,将加权残量迭代、估计停滞步长与感知矩阵条件数优化三种加快收敛速度的思想相结合,既利用了布雷格曼迭代在低信噪比条件下的重构能力又能保证快速成像.最后仿真实验结果表明在欠采样和低信噪比条件下本文算法能够缩短成像时间,且具备更好的噪声鲁棒性.