一种基于稀疏分解的微多普勒频率估计算法

目标微动特性和微多普勒特征分析在真假目标识别方面发挥了重要作用。由于微动目标雷达同波具有非线性、多分量性等特征,需要相应的具有高分辨力、低交叉项、大的动态范围的分析工具,才能较好地揭示目标微多普勒特征。稀疏分解方法中的匹配追踪（MP）具有频域高分辨能力,对于信号细微特征提取具有很好的效果。研究了基于匹配追踪的微多普勒频率估计问题,该方法可以准确提取出目标微多普勒频率,为后续的目标识别提供了重要的依据。-     

直升机的RCS计算

RCS（Radar Cross Section）是反映目标雷达散射特性的一个重要参数。一般把雷达目标除质心平动之外的转动,小幅振动和其它高阶运动统称为微动。主要研究旋翼转动情况下的直升机RCS计算。通过对直升机模型进行可视化计算即一种将目标建模与散射特性计算合为一体的交互式计算,使用图形算法（GRECO）和三维造型软件UG相结合的方法,这种方法与传统的RCS计算方法相比具有实时性好,效率高的优点,计算出直升机主体的RCS,再通过其加入调制的方法,计算得到直升机在旋翼转动条件下的RCS。最后给出了微动直升机的在具体入射情况下的单站RCS值。     

基于刚体微运动的弹体自旋多普勒模拟技术

能够模拟生成高速自旋弹体上终端接收信号多普勒的卫星导航信号模拟系统,可为基于卫星导航系统的低成本弹道修正技术开发提供有效地测试与评估手段。分析指出利用经典分段多项式方法对弹体自旋产生的接收信号多普勒进行模拟时,会出现模拟参数更新周期急剧减小,运算负荷显著增加的不足。针对这一问题,通过引入刚体微运动和微多普勒的概念,提出了基于刚体微运动的弹体自旋多普勒模拟技术,将弹体飞行过程中高速自旋产生的微多普勒特性参数作为模拟参数,有效解决了多普勒模拟参数更新周期减小的问题。仿真结果表明,对自旋频率为100 Hz的飞行弹体自旋多普勒进行模拟生成时,所提方法可在模拟参数更新周期为100ms时满足误差要求,远优于经典分段多项式方法小于1ms的要求。     

基于拖曳式干扰机的ISAR微动散射波干扰方法

通过对微多普勒效应的研究,提出了一种新的逆合成孔径雷达（ISAR）散射波干扰方法。将拖曳式干扰机和ISAR接收机分别等效为双基ISAR的发射站和接收站,干扰机对截获的ISAR信号进行微动信息调制并转发至目标,由其散射至ISAR接收机产生散射波干扰效果。干扰信号经ISAR接收机处理后可在真实目标回波成像结果附近产生假目标,且在方位向形成干扰条带。实验结果表明：通过控制干扰机转发参数及微动调制参数可分别实现不同的压制干扰效果。由于拖曳式干扰机与目标距离较近,干扰信号可获得较大功率,且与真实目标回波相参,可获得ISAR二维脉冲压缩处理增益,与传统射频噪声压制干扰方法相比成本较小。     

GEO星弹双基SAR多普勒分辨率特性分析

通过对地球同步轨道（GEO）卫星运动特点的分析，阐明其对多普勒分辨率的影响。传统的双基合成孔径雷达（SAR）多普勒分辨率表达式由于不考虑雷达平台加速度矢量带来的影响，并不适用于GEO星弹双基SAR系统。根据收发平台的双曲线轨迹对目标的回波多普勒特性影响，首先利用梯度方法推导了GEO星弹双基SAR系统的多普勒分辨率表达式；随后详细地分析了GEO卫星与导弹的空间几何关系及多普勒参数，特别是加速度矢量对多普勒分辨率的影响。仿真结果验证了所推导多普勒分辨率表达式的有效性，其有助于双基SAR理论系统完整性的提高，为后续系统设计及应用实践提供理论支撑。     

基于时频分析的双通道SAR自旋目标检测

强地杂波背景给微动目标检测带来很大困难,为此在详细分析距离向压缩数据域自旋目标回波特性基础上,提出了基于双通道合成孔径雷达相位中心偏置天线(SAR/DPCA)和沿航迹干涉(ATI)杂波抑制的两类自旋目标检测方法,并作比较分析.在DPCA模式下,微多普勒频率沿频率(m-D)轴有一整体平移量,其与目标自旋中心的方位向坐标成...     

基于修正离散Chirp-Fourier变换的高速目标ISAR距离像补偿

分析了高速运动目标的逆合成孔径雷达（ISAR）回波信号模型和高速运动对ISAR信号处理的影响。结果表明,高速运动特征会导致目标一维距离像的畸变和二维像的模糊。为此,提出了一种基于修正离散Chirp Fourier变换（MDCFT）的高速运动目标距离像补偿算法,采用MDCFT谱包络最小Shannon熵准则进行参数估计,可解决幅度最大准则下参数估计性能恶化的问题。仿真结果表明了该距离像补偿算法的有效性。     

调频连续波SAR改进的频率尺度变换算法(英文)

本文提出了一种调频连续波SAR改进的频率尺度变换算法。调频连续波SAR在运动过程中持续不断地发射和接收信号,这种运动导致了回波信号的伸缩,对波前重建产生了严重的影响。推导了回波信号模型,给出了信号的距离-多普勒域表达式,分析了由于天线不断运动而导致的相位变化与方位向频率之间的关系。改进的频率尺度变换算法补偿了这种相位变化,实现了目标的精确成像,仿真结果表明了分析的正确性和算法的有效性。     

洗出算法在控制补偿下的多通道结构优化

飞行模拟器的模拟逼真度在一定程度上取决于经典洗出算法的参数好坏，而其参数固定、信号缺失等弊端使洗出运动存在相位失真、延迟和错误暗示等诸多问题，导致飞行模拟逼真度不足。本文设计了多通道变参数滤波器结合模糊控制器实时改变其固定的截止频率；引入带通滤波器将输入信号与平动高频、中频、低频信号差分后经过二次滤波补偿缺失信号；结合平动高频信号中掺杂少量的低频信号经过滤波、角度转换环节补偿洗出角位移。通过Simulink建模仿真对改进算法进行验证，结果表明，改进后的算法较经典洗出算法平台运动空间更大、持续加速度模拟度更高，优化了相位延迟、错误暗示等问题，提升了飞行模拟逼真度。     

载体角运动对旋转捷联惯导误差的影响分析

旋转捷联惯导系统采用旋转调制误差补偿技术对陀螺仪和加速度计误差进行调制,可以提高系统导航精度。在简要分析旋转调制误差补偿机理基础上,研究了单轴旋转方案中载体常值旋转和周期旋转2种角运动模式对导航误差的影响。结果表明：载体特定角运动对旋转捷联惯导的误差补偿效果有一定影响,且单轴正反转停方案中误差补偿效果所受影响相对较小。     

Micro-Doppler effect in radar: phenomenon, model, and simulation study

When, in addition to the constant Doppler frequency shift induced by the bulk motion of a radar target, the target or any structure on the target undergoes micro-motion dynamics, such as mechanical vibrations or rotations, the micro-motion dynamics induce Doppler modulations on the returned signal, referred to as the micro-Doppler effect. We introduce the micro-Doppler phenomenon in radar, develop a model of Doppler modulations, derive formulas of micro-Doppler induced by targets with vibration, rotation, tumbling and coning motions, and verify them by simulation studies, analyze time-varying micro-Doppler features using high-resolution time-frequency transforms, and demonstrate the micro-Doppler effect observed in real radar data.     

结合核相关滤波器和深度学习的运动相机中无人机目标检测

针对无人机与相机快速相对运动造成的运动模糊问题，以及小型无人机外观信息缺失和背景复杂造成漏警和虚警问题，提出了一种新的无人机检测-跟踪方法。针对成像尺寸小于32像素×32像素的无人机目标，提出改进的多层特征金字塔的分类和目标框回归器作为目标检测器，克服漏警。利用检测结果初始化基于核相关滤波的目标跟踪器，并持续修正跟踪结果，跟踪结果为剔除检测器虚警提供依据。在跟踪过程中，引入对观测场景纹理自适应的相机运动补偿策略实现目标重定位。多场景下的实验结果表明：提出的方法在对高速运动小目标的检测和跟踪指标上显著优于传统方法，且运动补偿机制的引入进一步增强了方法在极端复杂场景下的鲁棒性。     

High-precision pose measurement method in wind tunnels based on laser-aided vision technology

The measurement of position and attitude parameters for the isolated target from a highspeed aircraft is a great challenge in the field of wind tunnel simulation technology.In this paper,firstly, an image acquisition method for small high-speed targets with multi-dimensional movement in wind tunnel environment is proposed based on laser-aided vision technology.Combining with the trajectory simulation of the isolated model, the reasonably distributed laser stripes and selfluminous markers are utilized to capture clear images of the object.Then, after image processing,feature extraction, stereo correspondence and reconstruction, three-dimensional information of laser stripes and self-luminous markers are calculated.Besides, a pose solution method based on projected laser stripes and self-luminous markers is proposed.Finally, simulation experiments on measuring the position and attitude of high-speed rolling targets are conducted, as well as accuracy verification experiments.Experimental results indicate that the proposed method is feasible and efficient for measuring the pose parameters of rolling targets in wind tunnels.     

空间进动目标动态散射特性的实验研究

研制了进动目标模型,构建了紧凑场微波暗室动态测量系统,通过暗室实验的方法研究了空间进动目标的动态散射特性,给出了若干典型条件下的全极化宽带测量结果。通过实验可以观察到目标进动引起的多普勒频移,证明了微波雷达对进动目标微多普勒的可观测性,同时观察到弹头常见结构引起的非理想点散射现象。实验结果的分析表明,该实验系统能够有效地揭示进动目标的目标特性和回波调制特性,从而为弹道中段目标的动态电磁散射特性、目标结构反演、运动参数提取和逆合成孔径雷达(ISAR)成像的研究奠定了基础,对于弹道中段目标识别的研究具有重要的意义。     

空间自旋目标宽带雷达干涉三维成像方法

空间目标三维成像可为目标的特征提取、分类与识别提供重要依据。基于L型三天线干涉成像原理,提出了一种宽带雷达条件下空间自旋目标干涉三维成像方法。首先,分析了雷达发射线性调频(LFM)信号条件下,空间自旋目标在距离-慢时间平面上的成像特点,建立了基于距离-慢时间平面的空间自旋目标干涉三维成像模型;其次,针对建立的干涉三维成像模型中,不同散射点的回波在距离-慢时间平面上会相互交叠的问题,对回波曲线分离、交叉点处理以及一维距离旁瓣的影响等进行了讨论,并给出了解决方法,从而获得目标三维图像。与已有方法相比,该方法可有效克服单基雷达三维成像无法获得目标各散射点真实三维位置以及在双/多基雷达三维成像时多部雷达回波联合处理较困难的问题。最后,仿真实验结果验证了所提方法的有效性。     

Target motion compensation in synthetic aperture radar

In a synthetic aperture radar (SAR) targets on the ground that are moving become smeared as a result of velocity components parallel to the motion of the radar and are moved to radically different angular positions if they have velocity components perpendicular to the motion of the radar. Methods for restoring moving targets to their correct size and position are described. The samples collected for SAR processing are frequency-modulated RF pulses. Mathematically this leads to spectra that are described by Fresnel integrals. For stationary targets, the spectrum is symmetrical around the origin. If there is a moving target in a range cell, its Doppler spectrum will be displaced from the origin and may undergo other changes as a result of its nonzero velocity. Proper compensation to locate the target at the correct position requires that the spectrum be translated to a position dependent on the along-track velocity rather than to the origin. From the central frequency, the along-range velocity component can be estimated and the length of the translation can then be found. After translation, the spectrum is converted back to the time domain and the customary SAR processing is performed     

基于超短基线声传感器阵的高速目标轨迹估计

针对低空高速飞行目标跟踪问题，首先研究了某典型目标噪声信号的时频特性，发现其信号呈现宽带低频特征，难以从频域对目标轨迹进行估计。在此基础上，从各路接收信号的到达时延量入手，考虑到声基阵只能布设于有限空间内的制约，提出了一种基于超短基线阵时延估计的目标跟踪方法。该方法利用各个超短基线阵接收声强极值点分别估计目标运动轨迹垂线方向，计算多个垂线的叉乘向量实现对目标运动方向的估计，再利用多面交汇的方式获估计得到目标运动轨迹。分别对目标俯仰角、方位角及运动轨迹估计的理论误差进行了推导，根据理论估计误差，为能够实现对目标运动轨迹的估计，各个超短基线阵应尽量保证与目标运动轨迹不在同一平面上。根据仿真结果，在采用4个传感器基阵时，角度估计平均误差在4°以内，位置估计相对误差在5%左右。仿真结果验证了该方法的有效性。     

Moving target imaging algorithm for SAR data

A digital processing algorithm for fine-resolution imaging of synthetic aperture radar (SAR) moving targets is described. The targets may have any translational and rotational motion components relative to the data collection platform. The algorithm requires the presence of up to three prominent points in the image of the target; the signals from these points provide estimates of the unknown target motion parameters. Phase compensation and data formatting based on these estimates eliminate motion-induced phase errors. This algorithm has been implemented on a VAX computer and used to process both simulated and real SAR data of moving targets. Results obtained using the simulated data are presented     

基于机器学习的空间翻滚目标实时运动预测

借助监督式机器学习（ML）方法,对空间翻滚目标的运动状态预测问题进行研究,为空间机器人抓捕空间翻滚目标提供可靠的数据依据。基于物理模型的运动预测方法依赖理想的建模假设,需要连续的视觉反馈信息,解决目标预测问题的能力有限。因此,本文采用机器学习中纯数据驱动方式的稀疏伪输入高斯过程（SPGP）回归方法进行空间翻滚目标的运动预测。给定空间翻滚目标运动状态的历史观测数据,通过连续优化真实观测数据,得到稀疏的伪训练数据集,进而在线快速预测目标的运动状态,预测的计算效率达到毫秒级。此外,利用马尔科夫链蒙特卡洛（MCMC）法处理连续优化过程,克服由于随机初始值造成的优化过程陷入局部极小值问题。利用Snelson数据验证了所提稀疏伪输入高斯过程回归方法的正确性,并通过4组仿真算例验证了所提方法对于空间翻滚目标运动预测的有效性和鲁棒性。     

Track labeling and PHD filter for multitarget tracking

Multiple target tracking requires data association that operates in conjunction with filtering. When multiple targets are closely spaced, the conventional approaches (as, e.g., MHT/assignment) may not give satisfactory results. This is mainly because of the difficulty in deciding what the number of targets is. Recently, the probability hypothesis density (PHD) filter has been proposed and particle filtering techniques have been developed to implement the PHD filter. In the particle PHD filter, the track labeling problem is not considered, i.e., the PHD is obtained only for a frame at a time, and it is very difficult to perform the multipeak extraction, particularly in high clutter environments. A track labeling method combined with the PHD approach, as well as considering the finite resolution, is proposed here for multitarget tracking, i.e., we keep a separate tracker for each target, use the PHD in the resolution cell to get the estimated number and locations of the targets at each time step, and then perform the track labeling ("peak-to-track" association), whose results can provide information for PHD peak extraction at the next time step. Besides, by keeping a separate tracker for each target, our approach provides more information than the standard particle PHD filter. For example, in group target tracking, if we are interested in the motion of a specific target, we can track this target, which is not possible for the standard particle PHD filter, since the standard particle PHD filter does not keep track labels. Using our approach, multitarget tracking can be performed with automatic track initiation, maintenance, spawning, merging, and termination