PHASE AUTOFOCUSING ALGORITHM FOR COMPRESSED INVERSE SYNTHETIC APERTURE RADAR IMAGING

As same as the conventional inverse synthetic aperture radar（ISAR）, the compressed ISAR also requires the echo signal based motion compensation, which consists of the range alignment and the phase autofoeusing. A phase autofocusing algorithm for compressed ISAR imaging is presented. In the algorithm, phase autofocusing for the sparse ISAR echoes is accomplished using the eigenvector method. Experimental results validate the effectiveness of the algorithm.

A COMPARISON OF SOME ELECTRONIC COUNTERMEASURES ON INVERSE SYNTHETIC APERTURE RADAR (ISAR)

Inverse Synthetic Aperture Radar (ISAR) is an important means for target classification, recognition, identification and many other military applications. A simulation model of ISAR system is established after analyzing the principle of ISAR imaging, and then several ECM (Electronic Counter Measurement) techniques are studied. Simulation experiments are done on the basis of such research. The experimental result of the research can be used for ECM equipment.

Micro-motion effect in inverse synthetic aperture radar imaging of ballistic mid-course targets

For ballistic mid-course targets,in addition to constant orbital motion,the target or any structure on the target undergoes micro-motion dynamics,such as spin,precession and tumbling.The micro-motion characteristics of the ballistic mid-course targets were discussed.The target motion model and inverse synthetic aperture radar(ISAR) imaging model for this kind of targets were built.Then,the influence of micro-motion on ISAR imaging based on the established imaging model was presented.The computer simulation to get mid-course target echoes from static darkroom electromagnetic scattering data based on the established target motion model was realized.The imaging results of computer simulation show the validity of ISAR imaging analysis for micro-motion targets.

Distributed inverse synthetic aperture radar imaging of ship target with complex motion

For ship targets with complex motion,it is difficult for the traditional monostatic inverse synthetic aperture radar(ISAR)imaging to improve the cross-range resolution by increasing of accumulation time.In this paper,a distributed ISAR imaging algorithm is proposed to improve the cross-range resolution for the ship target.Multiple stations are used to observe the target in a short time,thereby the effect of incoherence caused by the complex motion of the ship can be reduced.The signal model of ship target with three-dimensional(3-D)rotation is constructed firstly.Then detailed analysis about the improvement of crossrange resolution is presented.Afterward,we propose the methods of parameters estimation to solve the problem of the overlap or gap,which will cause a loss of resolution and is necessary for subsequent processing.Besides,the compressed sensing(CS)method is applied to reconstruct the echoes with gaps.Finally,numerical simulations are presented to verify the effectiveness and the robustness of the proposed algorithm.

Inverse synthetic aperture radar range profile compensation of plasma-sheath-enveloped reentry object

The scattering points in a plasma sheath characterized with coupled velocities can cause pulse compression mismatching,which results in displacement and energy diffusion in the onedimension range profile.To solve this problem,we deduce the echo model of the plasma-sheathenveloped reentry object.By estimating the coupled velocities,we propose a compensation method to correct the defocus of an inverse synthetic aperture radar(ISAR)image in range dimension to improve the quality of the ISAR images.The simulation results suggest that the echoes from different regions of the surface of the reentry object have various coupling velocities,and the higher the coupled velocity,the more serious the displacement and energy diffusion in the range dimension.Our proposed method can correct the range dimension aberration.Two measurement metrics were used to evaluate the improvement of the compensation method.

一种联合TDCM和PFA的空间目标大转角ISAR成像方法

空间目标态势感知是当前航天技术领域的研究热点,逆合成孔径雷达(Inverse Synthetic Aperture Radar, ISAR)是空间目标感知的一种有效手段。为了得到利于目标鉴别的高分辨二维ISAR图像,需要通过增大积累转角来提高方位向分辨率。但是,随着转角的增大,平动与转动之间的耦合增大,使得传统的先平动补偿、后利用转动成像的两步式成像方法不再适用。本文提出了一种结合时域信号共轭相乘(Time Domain Conjugate Multiplication, TDCM)处理与极坐标格式算法(Polar Format Algorithm, PFA)的成像算法,首先利用雷达回波的二维相干性,通过TDCM处理进行平动粗补偿,然后借助PFA的插值操作校正大转角引起的散射点越分辨单元走动(Motion Through Resolution Cell, MTRC)并估计目标相对转动参数。通过迭代处理,持续对平动和转动参数进行优化,提升运动补偿效果。仿真验证了所提成像算法的有效性,与传统算法相比,成像聚焦性更好。

基于时空注意力-Seq2Seq网络的ISAR包络对齐方法

包络对齐是逆合成孔径雷达(Inverse Synthetic Aperture Radar,ISAR)成像中平动补偿处理的第一步,包络对齐的精度对于方位聚焦和成像质量具有重要影响。针对稀疏孔径和低信噪比条件下传统的包络对齐算法性能显著降低的问题,本文提出一种基于时空注意力-Seq2Seq网络的包络对齐方法。该网络模型以门控循环单元为编码解码单元,针对点目标距离像包络的能量分布特征对空间注意力机制进行改进后,添加时间和空间两维注意力机制形成对ISAR距离像回波包络进行对齐的能力。数据生成方面,基于电磁波仿真参数和目标运动仿真参数进行成像模拟仿真构造了ISAR回波数据集,经过8倍插值后输入网络进行训练,使网络学习到从未对齐回波到对齐回波的映射关系。所提方法以离线训练代替在线相关计算,融合了Seq2Seq模型在处理序列到序列问题上的结构优势、时间注意力机制在捕捉长期依赖关系和空间注意力机制在提取区域特征上的突出能力,实现了稀疏孔径和低信噪比条件下对距离-慢时间域ISAR回波的自动对齐。通过向训练好的时空注意力-Seq2Seq网络输入未对齐的回波序列,网络可以在不改变回波相位结构的前提下自动实现包络对齐。仿真和实测数据对齐结果表明,和传统的包络对齐方法相比,所提方法在稀疏孔径和低信噪比条件下优势明显,在欠采样率为50%、信噪比为0 dB条件下对雅克-42飞机实测回波数据的包络对齐实验中,该方法将循环移位误差由39、26减小至6,将成像结果的图像熵由4.58、4.22减小至1.71,验证了其良好性能。

一种基于时间编码超表面的ISAR干扰方法

时间编码超表面具备对电磁波的调制能力,从而有效改变反射电磁波的特性,达到改变目标特性的目的。基于超表面的调控特性,设计了一种周期时间编码的逆合成孔径雷达(Inverse Synthetic Aperture Radar,ISAR)干扰方法。分析了时间编码超表面的相位调制机理,建立了时间编码序列模型,通过设计优化时间编码序列的相关参数,形成对雷达在距离向上的欺骗干扰。通过理论推导,分析了时间编码调制参数对假目标位置、幅度等的影响。仿真实验结果表明,该方法可以根据信号参数生成数量可控、位置可变的虚假目标,距离向干扰范围扩展至2倍以上,从而实现不同的干扰效果,在电子对抗等方面具有较高的应用价值。

基于ISAR成像的非合作航天器位姿识别网络

非合作航天器缺乏合作信息,无法直接利用传感器获得位姿数据,提出一种基于ISAR图像的位姿识别网络.相比于空间摄影卫星拍摄的图像以及仿真数据,该图像更易获取、成本更低,但存在分辨率低、面板成像不完整等问题.因此,该网络在图像预处理时,通过对YOLOX-tiny的调整,将其作为航天器裁剪网络,避免图像中标记的数据影响后续网络的训练,使网络仅关注航天器所在区域.利用增强的Lee滤波滤除图像噪声,提升图像的质量.在骨干网络中,加入STN模块,使网络选择最相关的区域注意,将U-Net网络设计成密集残差块结构并结合CBAM模块,减少下采样期间的特征损失,提高模型的准确性.此外,引入了多头自注意力来捕获更多的全局信息.实验结果表明,该模型最小、最大、平均误差较于目前的一些主流模型均有所提升,误差缩小了0.5-0.6,从而证明该网络具有更好的位姿识别能力.

空间目标ISAR图像三维基元表示方法

空间卫星目标的逆合成孔径雷达(ISAR)图像由离散的散射点构成,具有弱纹理、高动态、非连续的特性,造成传统算法对空间目标ISAR图像进行三维重建时,得到的点云结果稀疏,无法覆盖目标整体外形轮廓,进而导致难以精确提取目标结构、姿态参数。针对上述问题,并考虑到空间目标通常由特定模块化部件组成的特点,该文提出一种从空间目标ISAR图像中抽取参数化基元来表示其三维结构的方法。首先利用能量累积算法从ISAR图像中得到目标的稀疏点云,进而利用参数化基元对点云进行拟合,最后将基元投影至ISAR成像平面,并最大化与目标图像的相似度来优化基元参数,得到最优的目标三维基元表示。相比于传统点云三维重建,该方法能够获得对目标三维结构更完整的描述,且所得到的基元参数即代表目标的姿态及结构,可直接支撑后续的目标识别、分析研判等任务。仿真实验证明该方法能够根据ISAR序列图像,有效实现对空间目标的三维表示。

结合最近邻图模型的稀疏ISAR成像方法

逆合成孔径雷达(Inverse Synthetic Aperture Radar,ISAR)稀疏成像方法可提供图像对比度高、旁瓣干扰少的成像结果 .稀疏成像以场景或目标散射率分布具有稀疏性为前提,待成像目标场景的稀疏特性决定了最终成像质量. ISAR目标场景的自然稀疏特性着重刻画点状特征,变换域稀疏表示可增强目标图像的纹理等通用特征.通过学习获得的稀疏变换字典,可自适应于待成像的ISAR目标场景,找到面向ISAR目标图像块的特有稀疏表示.但是,图像块的特有稀疏表示中忽略了待成像目标场景中目标的几何特征信息.最近邻图模型可建立给定数据的几何特征描述算子,刻画出给定数据的几何特征信息.本文利用最近邻图模型来刻画待成像目标场景中目标的几何特征信息,并映射到待成像目标场景的特有稀疏表示中;提出结合最近邻图模型的ISAR稀疏成像方法,用于不同类别实测ISAR数据成像.相比已有的ISAR稀疏成像方法,所提成像方法可获得目标轮廓更清晰的成像结果,成像所需时间平均减少10.4%.

False-target image synthesizer for countering ISAR via inverse dechirping

For real-time jamming signal generation in deceiving inverse synthetic aperture radar（ISAR）,the target characteristics modulation is always processed in the expensive field programmable gate array（FPGA）.Due to the large computational complexity of the traditional modulating operation,the size and structure of simulated false-target are limited.With regard to the principle of dechirping in range compression of linear frequency modulated（LFM） radar,a novel algorithm named ＂inverse dechirping＂ is proposed for target characteristics modulation.This algorithm only needs one complex multiplier in the FPGA to generate the jamming signal when the radar signal is intercepted,which can be obtained by multiplication of radar signal samplings and the equivalent dechirped target echo in the time domain.As the complex synthesis of dechirped target echo can be realized by cheap digital signal processor（DSP） within the interpulse time,the overall cost of the jamming equipment will be reduced and the false-target size will not be limited by the scale of FPGA.Numerical simulations are performed to verify the correctness and effectiveness of the proposed algorithm.

基于变分模态分解与优选的超高分辨ISAR成像微多普勒抑制方法

逆合成孔径雷达(ISAR)在对空中目标成像时,目标自身的转动、振动等局部微动将产生微多普勒效应,回波将附加额外的多普勒调制,造成频谱展宽。在超高分辨条件下,这一微动特性将会影响主体散射点的聚焦,导致目标图像局部散焦模糊,严重影响成像质量。并且,微多普勒相位还具有时变非平稳特性,难以从ISAR目标回波中准确估计或分离出微多普勒。为了解决上述问题,该文利用目标主体回波和微多普勒分量的时频分布差异,提出一种基于变分模态分解(VMD)与优选的非参数化方法抑制了回波中的微多普勒分量,消除了微多普勒对成像的影响,获得超高分辨率的无人机ISAR成像结果。该文首先引入VMD算法并将其扩展到复数域,将ISAR目标回波数据沿方位向分解为若干个中心频率均匀分布于多普勒采样带宽中的模函数,在此基础上利用图像熵指标优化分解参数和筛选成像模态,以保证微多普勒的良好抑制和主体回波的较完整保留。与现有基于经验模态分解(EMD)和局部均值分解(LMD)的方法相比,所提方法在超大带宽条件下对旋翼微动引起的微多普勒干扰有着更为出色的抑制效果,而且对机身部分的保留更为完整。最后,通过仿真对比和超宽带微波光子ISAR无人机实测数据处理,证明了该文所提方法的有效性和优势。

Multi-static InISAR imaging for ships under sparse aperture

This paper concentrates on super-resolution imaging of the ship target under the sparse aperture situation.Firstly,a multi-static configuration is utilized to solve the coherent processing interval(CPI)problem caused by the slow-speed motion of ship targets.Then,we realize signal restoration and image reconstruction with the alternating direction method of multipliers(ADMM).Furthermore,we adopt the interferometric technique to produce the three-dimensional(3D)images of ship targets,namely interferometric inverse synthetic aperture radar(InISAR)imaging.Experiments based on the simulated data are utilized to verify the validity of the proposed method.

An empirical method for joint inversion of wave and wind parameters based on SAR and wave spectrometer data

Synthetic aperture radar(SAR)and wave spectrometers,crucial in microwave remote sensing,play an essential role in monitoring sea surface wind and wave conditions.However,they face inherent limitations in observing sea surface phenomena.SAR systems,for instance,are hindered by an azimuth cut-off phenomenon in sea surface wind field observation.Wave spectrometers,while unaffected by the azimuth cutoff phenomenon,struggle with low azimuth resolution,impacting the capture of detailed wave and wind field data.This study utilizes SAR and surface wave investigation and monitoring(SWIM)data to initially extract key feature parameters,which are then prioritized using the extreme gradient boosting(XGBoost)algorithm.The research further addresses feature collinearity through a combined analysis of feature importance and correlation,leading to the development of an inversion model for wave and wind parameters based on XGBoost.A comparative analysis of this model with ERA5 reanalysis and buoy data for of significant wave height,mean wave period,wind direction,and wind speed reveals root mean square errors of 0.212 m,0.525 s,27.446°,and 1.092 m/s,compared to 0.314 m,0.888 s,27.698°,and 1.315 m/s from buoy data,respectively.These results demonstrate the model’s effective retrieval of wave and wind parameters.Finally,the model,incorporating altimeter and scatterometer data,is evaluated against SAR/SWIM single and dual payload inversion methods across different wind speeds.This comparison highlights the model’s superior inversion accuracy over other methods.

基于调频斜率变化的ISAR成像干扰方法研究

为对抗先进体制逆合成孔径雷达(inverse synthetic aperture radar,ISAR)等成像识别雷达,提出基于调频斜率变化的ISAR成像干扰新方法。首先,根据ISAR宽带线性调频(linear frequency modulation,LFM)信号模型及解线频调信号处理流程,分析调频斜率变化对ISAR成像结果的影响,揭示其变化特性可用于干扰的基本机理。然后,利用LFM信号带宽、脉宽在一定范围内伪随机变化,形成调频斜率伪随机变化的ISAR干扰信号,与雷达回波信号混合进入雷达接收机形成干扰。最后,利用仿真实验验证调频斜率变化的影响以及所提干扰方法在ISAR成像干扰方面的有效性。

基于Multitask⁃YOLO网络的卫星帆板ISAR图像快速分割

随着空间技术的飞速发展,空间态势感知能力需求不断增加。与传统光学传感器相比,逆合成孔径雷达(Inverse synthetic aperture radar,ISAR)具有全天候、远距离高分辨率成像的能力,且成像不受光照条件的影响。此外,空间态势感知系统需要对周围航天器进行准确的评估,因此对空间目标部件识别能力的需求日益迫切。本文提出了一种基于YOLOv5结构的Multitask⁃YOLO网络,用于卫星ISAR图像中卫星帆板的识别和分割。首先,本文添加了分割解耦头来实现网络的分割功能。然后用空间金字塔池快速算法(Spatial pyramid pooling fast,SPPF)和距离交并比算法(Distance intersection over union,DIoU)代替原有结构,避免图像失真,加快收敛速度。通过在通道中引入注意机制,提高了分割和识别的准确性。最后使用模拟卫星的ISAR图像进行实验。结果表明,所提出的Multitask⁃YOLO网络高效、准确地实现了部件的识别和分割。与其他的识别和分割网络相比,该网络的平均精度(mean Average precision,mAP)和平均交并比(mean Intersection over union,mIoU)提高了约5%。此外,该网络的运行速度高达16.4 GFLOP,优于传统的多任务网络的性能。

天基ISAR对空间轨道目标高分辨率成像问题分析

对空间轨道目标高分辨率成像是空间态势感知计划的重要组成部分。本文讨论天基逆合成孔径雷达(inverse synthetic aperture radar,ISAR)对空间目标进行高分辨率成像的概念。基于雷达和空间轨道目标相对运动场景,研究减少天基ISAR回波数据量的方法;分析天基ISAR对空间目标可成像时间段的限制因素,推导天基ISAR与目标轨道高度差及天基ISAR波束扫描角度的噪声等效后向散射系数(noise equivalent sigma zero,NESZ)公式,探讨满足方位分辨率的天基ISAR在轨可成像时间段,并利用NESZ衡量可成像时刻内回波信噪比(signal to noise ratio,SNR),为天基ISAR系统设计提供依据。此外,还推导天基ISAR对空间轨道目标回波表达式,并研究二维图像散焦原因,提出ISAR图像二维相位误差具体补偿模型,通过仿真结果验证了分析的正确性。

基于调频率微调的压缩感知ISAR抗欺骗干扰技术研究

有源欺骗干扰是针对逆合成孔径雷达(ISAR)的一种较成熟的干扰技术,但抗欺骗干扰技术研究尚有欠缺。文中在分析欺骗干扰信号生成原理的基础上,采用脉间调频率微调的方法,使得虚假目标信号与雷达解线频调脉压参考信号的调频率失配,以降低虚假目标的脉压增益。但由于调频率脉间随机变化,会造成成像积累时间内不同调频率回波信号无法相参积累,难以进行方位向高分辨成像的问题。为解决这个问题,提出了采用压缩感知技术,对调频率相同的稀疏回波进行信号重构,以获取真实目标的ISAR图像。此外,利用干扰信号调频率失配时的距离像特性,文中还提出基于距离像熵值的智能化干扰感知方法,能够准确判断雷达是否遭受干扰,为雷达决策采用抗干扰工作模式提供依据。仿真结果表明所提方法可以准确感知干扰,能够在有源欺骗干扰情况下抑制假目标回波,得到真实目标清晰的ISAR像。

基于深度增强IST网络的ISAR稀疏成像

针对传统逆合成孔径雷达稀疏成像算法存在参数敏感、收敛速度慢等问题,文章以卷积神经网络的自适应参数学习机制为基础,结合模型驱动网络的物理可解释性,提出了一种ISAR稀疏成像架构——深度增强迭代收缩阈值(Deep Augmented-Iterative Shrinkage Thresholding,DA-IST)网络。首先,DA-IST网络将迭代收缩阈值算法的迭代步骤映射至隐藏层中,不仅能够提高可解释性,而且能够在训练过程中学习最优参数;其次,网络在建模过程中考虑了被忽略的高频分量,提高了重构性能;同时,为了提高网络的鲁棒性,用非线性卷积层替代了线性稀疏变换。实验表明,与传统的模型驱动算法相比,DA-IST网络避免了人工调整参数过程,收敛速度更快,成像质量更高,对特征差异较大的数据具有更好的泛化能力。