Three-dimensional positions of scattering centers reconstruction from multiple SAR images based on radargrammetry

A method and procedure is presented to reconstruct three-dimensional(3D) positions of scattering centers from multiple synthetic aperture radar(SAR) images. Firstly, two-dimensional(2D) attribute scattering centers of targets are extracted from 2D SAR images. Secondly, similarity measure is developed based on 2D attributed scatter centers' location, type, and radargrammetry principle between multiple SAR images. By this similarity, we can associate 2D scatter centers and then obtain candidate 3D scattering centers. Thirdly, these candidate scattering centers are clustered in 3D space to reconstruct final 3D positions. Compared with presented methods, the proposed method has a capability of describing distributed scattering center, reduces false and missing 3D scattering centers, and has fewer restrictionson modeling data. Finally, results of experiments have demonstrated the effectiveness of the proposed method.

Hybrid micromotion-scattering center model for synthetic aperture radar micromotion target imaging

Micromotion is an important target feature, although the target micromotion has an unfavorable influence on the synthetic aperture radar （SAR） image interpretation due to defocusing. This paper introduces micromotion parameters into the scattering center model to obtain a hybrid micromotion-scattering center model, and then proposes an optimization algorithm based on the maximal likelihood estimation to solve the model for jointly obtaining target motion and scattering parameters. Initial value estimation methods using targets＇ ghost images are then presented to guarantee the global and fast convergence. Simulation results show the effectiveness of the proposed algorithm especially in high precision estimation and multiple targets processing.

Scattering center modeling for low-detectable targets

The scattering centers(SCs)of low-detectable targets(LDTs)have a low scattering intensity.It is difficult to build the SC model of an LDT using the existing methods because these methods mainly concern dominant SCs with strong scattering contributions.This paper presents an SC modeling approach to acquire the weak SCs of LDTs.We employ the induced currents at the LDT to search SCs,and the joint time-frequency transform together with the Hough transform to separate the scattering contributions of different SCs.Particle swarm optimization(PSO)is applied to improve the estimation results of SCs.The accuracy of the SC model built by this approach is verified by a full-wave numerical method.The validation results show that the SC model of the LDT can precisely simulate the signatures of high-resolution images,such as high-resolution range profile and inverse synthetic aperture radar(ISAR)images.

Time-domain compressive dictionary of attributed scattering center model for sparse representation

Parameter estimation of the attributed scattering center(ASC) model is significant for automatic target recognition(ATR). Sparse representation based parameter estimation methods have developed rapidly. Construction of the separable dictionary is a key issue for sparse representation technology. A compressive time-domain dictionary(TD) for ASC model is presented. Two-dimensional frequency domain responses of the ASC are produced and transformed into the time domain. Then these time domain responses are cutoff and stacked into vectors. These vectored time-domain responses are amalgamated to form the TD. Compared with the traditional frequency-domain dictionary(FD), the TD is a matrix that is quite spare and can markedly reduce the data size of the dictionary. Based on the basic TD construction method, we present four extended TD construction methods, which are available for different applications. In the experiments, the performance of the TD, including the basic model and the extended models, has been firstly analyzed in comparison with the FD. Secondly, an example of parameter estimation from SAR synthetic aperture radar(SAR) measurements of a target collected in an anechoic room is exhibited. Finally, a sparse image reconstruction example is from two apart apertures. Experimental results demonstrate the effectiveness and efficiency of the proposed TD.

Synthetic aperture radar imaging based on attributed scatter model using sparse recovery techniques

The sparse recovery algorithms formulate synthetic aperture radar(SAR) imaging problem in terms of sparse representation(SR) of a small number of strong scatters' positions among a much large number of potential scatters' positions,and provide an effective approach to improve the SAR image resolution.Based on the attributed scatter center model,several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques,namely,sparse Bayesian learning(SBL),fast Bayesian matching pursuit(FBMP),smoothed l0 norm method(SL0),sparse reconstruction by separable approximation(SpaRSA),fast iterative shrinkage-thresholding algorithm(FISTA),and the parameter settings in five SR algorithms were discussed.In different situations,the performances of these algorithms were also discussed.Through the comparison of MSE and failure rate in each algorithm simulation,FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model.Although the SBL is time-consuming,it always get better performance when related to failure rate and high SNR.

空中目标动态电磁散射数据仿真系统设计与实现

电磁散射数据是目标识别研究的基础,但由于试验测量成本高、可重复性差等问题,空中目标电磁散射实测数据十分有限。基于去遮挡的N点模型和电磁计算数据插值研究了空中目标动态电磁散射数据仿真方法,设计了空中目标动态电磁数据仿真系统,该系统将电磁散射仿真与实际飞行场景相结合,支持场景自定义和空中目标三维模型库扩展。分析了飞机和巡航导弹2类目标仿真数据及成像结果,结果验证了仿真方法正确性和系统有效性,可为研究实际场景下的空中目标识别提供支撑。

基于物理启发机器学习的属性散射中心提取方法

基于参数化散射中心模型进行参数估计是实现合成孔径雷达高级信息获取(SAR AIR)技术的基本思路之一,传统的属性散射中心(ASC)参数估计算法往往具有计算速度慢、算法复杂度高、对参数初值要求高等问题。对此,该文提出一个新的基于无监督学习的端到端框架用于从SAR图像反演ASC参数。首先,利用自编码式网络结构有效提取目标图像特征,缓解由于优化空间复杂非凸导致的直接求解困难,解决初值敏感问题;其次,通过嵌入ASC模型作为物理解码器以将编码器输出约束为正确的ASC参数;最后,通过端到端的模型架构进行学习和推理,达到降低算法复杂度及提高估计速度的目的。通过在仿真和实测数据上进行测试,实验结果表明在0.15 m分辨率测试集SAR图像上取得低于0.1 m的估计误差,反演单个散射中心平均耗时0.06 s,验证了该文所提方法的有效性、高效性与鲁棒性。

基于RANSAC和三维谱峰分析的全姿态散射中心建模

全姿态散射中心模型是一种性能优良的光学区复杂目标电磁散射参数化模型。针对传统的基于候选点筛选和聚类的全姿态散射中心建模方法易出现虚假散射中心和遗漏真实散射中心的问题,该文提出了一种基于目标三维空间电磁散射强度场谱峰分析的建模方法。首先,基于目标多视一维散射中心参数,利用随机采样一致性(RANSAC)方法和Parzen窗函数方法估计目标在三维空间中的电磁散射强度场。然后,通过谱峰分析、散射中心关联和多视量测融合,得到全姿态三维散射中心的位置。最后,利用二值形态学处理修正全姿态散射中心的角度可见性,估计全姿态散射中心的散射系数和类型参数。仿真结果表明,该文方法所提取的全姿态散射中心与目标几何结构具有极强的关联性,相较传统方法,在缩减三维散射中心数量的同时提升了模型的表示精度。

目标近场电磁散射中心建模与特征参数反演

为了通过较少散射场数据实现雷达目标特性建模和参数反演,提出了一种基于时频图多普勒特征的散射中心建模和特征参数反演方法,采用单频点下的一维角度近场扫描数据实现对目标二维散射中心的提取。通过平滑伪Wigner-Ville分布(smooth and pseudo Wigner-Ville distribution,SPWVD)分析方法生成高分辨率时频图,根据不同类型散射中心的多普勒特征,通过Radon逆变换(inverse Radon transform,IRT)提取散射中心模型的位置和幅度参数,并将提取的散射中心位置参数与几何模型参数、散射中心模型重构雷达散射截面积(RCS)与仿真的RCS进行了性能对比。结果表明:该方法只需要单频点的一维角度扫描数据,即可有效提取目标散射中心位置和幅度等特征参数,且重构RCS的均方根误差小于3 dBsm。

结合三维电磁散射模型和深度学习的SAR目标识别框架设计

不断提高合成孔径雷达(SAR)图像目标识别能力对于全天时、全天候战场情报侦察具有重要意义。近年来,深度学习模型在SAR目标识别领域得到了广泛应用和验证,但由于SAR图像样本往往十分有限,模型的适应性受到一定制约。提出结合三维电磁散射模型和深度学习的SAR目标识别框架,充分运用三维电磁散射模型在目标SAR数据生成以及物理属性描述方面的优势,提升深度学习模型的分类可靠性和适应性。

HIGH RESOLUTION PARAMETRIC MODELLING FOR TWO-DIMENSIONAL RADAR TARGET USING PRONY ALGORITHM

On the conditions of low-resolution radar, a parametric model for two-dimensional radar target is described here according to the theory of electromagnetic scattering and the geometrical theory of diffraction. A high resolution estimation algorithm to extract the model parameters is also developed by building the relation of the scattering model and Prony model. The analysis of Cramer-Rao bound and simulation show that the method here has better statistical performance. The simulated analysis also indicates that the accurate extraction of the diffraction coefficient of scattering center is restricted by signal to noise ratio, radar center frequency and radar bandwidth.

Parameter estimation of GTD model and RCS extrapolation based on a modified 3D-ESPRIT algorithm

The noise robustness and parameter estimation performance of the classical three-dimensional estimating signal parameter via rotational invariance techniques(3D-ESPRIT)algorithm are poor when the parameters of the geometric theory of the diffraction(GTD)model are estimated at low signal-to-noise ratio(SNR).To solve this problem,a modified 3D-ESPRIT algorithm is proposed.The modified algorithm improves the parameter estimation accuracy by proposing a novel spatial smoothing technique.Firstly,we make cross-correlation of the auto-correlation matrices;then by averaging the cross-correlation matrices of the forward and backward spatial smoothing,we can obtain a novel equivalent spatial smoothing matrix.The formula of the modified algorithm is derived and the performance of this improved method is also analyzed.Then we compare root-meansquare-errors(RMSEs)of different parameters and the locating accuracy obtained by different algorithms.Furthermore,radar cross section(RCS)of radar targets is extrapolated.Simulation results verify the effectiveness and superiority of the modified 3DESPRIT algorithm.

Building and application of multipath model for meter-wave radar in elevation estimation

A research is done for multipath effects of low-angle tracking in meter-wave radar,and the theory of multi-scattering centers of complex target is discussed as well as the character of reflected echoes.This points out that the distribution and scattering properties of scattering centers are the prime reasons which cause the variation of multipath effects,and all the changes of position,motion and attitude of the target can influence the multipath effects.By building of multipath model for multi-scattering centers for target,the analysis above is verified and a new method of elevation estimation for low-angle target is presented.The new method uses canceling vectors obtained by searching to cancel reflected waves in echoes and reduce the influence of reflected components,which can improve the accuracy of elevation estimation of low-angle target and the performance of low-angle tracking in meter-wave radar.Experimental results verify the availability of the method.

薄涂覆目标散射中心的精确建模

目前,针对金属目标的散射中心模型的研究已相对成熟,但是对带有薄涂覆结构的复杂目标散射中心模型的研究较少。通过研究发现,薄涂覆引入的影响主要体现为对散射场幅度的衰减作用。因此,提出通过对薄涂覆对电磁波的衰减作用建模,并在金属目标散射中心模型的基础上增加该衰减项,以实现对薄涂覆目标的散射中心建模。该建模方法避免了直接对复杂目标整体散射进行数学建模和参数估计,散射模型的物理意义更明确、形式更简洁。经弹头和飞机目标的全波法结果验证,所建立的薄涂覆目标散射中心模型在雷达散射截面起伏、成像特征的仿真中均具有较高的精度。

基于扩展OTSM图的滑动型散射中心建模方法

雷达目标散射中心建模是雷达目标特性分析与雷达目标识别中的关键步骤,光滑流线型结构在雷达目标上的广泛应用,给传统散射中心建模带来了巨大挑战。该文针对滑动散射中心建模开展研究,首先分别基于曲面边缘散射和曲面散射两种情况,推导了滑动散射中心的位置表达式;其次,提出一种基于扩展1维-高维(2维/3维)散射映射图(One–Two/Three Dimensional Scattering Mapping,OTSM)的滑动散射中心估计方法,通过相邻视角的投影几何关系推导滑动散射中心的位置;然后,综合RANSAC算法获取的固定散射中心,获得目标完备的散射中心模型。利用暗室测量数据对算法进行了验证,结果表明了该文算法的有效性。

一种高频区复杂雷达目标二维散射的参数模型

为描述高频区雷达目标复杂的二维散射 ,提出采用更贴近散射机理的、特征含量更为丰富的二维散射中心模型———类型区分的几何绕射理论 (TD GTD)模型。将一种高效的基于AGR的参数估计初始值选取方法 ,以及引入RELAX技术的最大似然估计算法 (ML RELAX) ,用于模型参量的估计。解决了同时估计多个散射中心这一造成计算量过大的根源问题。实验证实了TD GTD模型的合理性以及ML

中段目标微运动建模方法与宽带雷达回波仿真

改进了中段目标微进动动力学参数模型,建立了微进动目标数学描述方程,从中段目标的外形特征、运动特征及散射特性全面阐述了中段目标的物理特性,提出了中段目标的移动散射点模型,最后给出了仿真结果,为中段目标识别提供了重要基础。

雷达目标散射中心模型反演及其在识别中的应用

提出了一种基于散射中心模型的距离像识别方法。首先采用稀疏空间网格上的宽带测量数据离线反演目标散射中心模型,再将从距离像中实时提取的一维散射中心特征与该散射中心模型的一维投影进行匹配完成在线识别。在散射中心模型反演中,提出了稳定散射中心的概念并基于此完成稀疏空间网格下的一维散射中心投影关联。在识别过程中,通过设计合适的匹配函数解决散射中心参数估计误差和模型误差造成的散射中心数目、幅度和位置不完全匹配问题。仿真实验表明,对于精度较高的模型,基于模型的识别方法与基于距离像模板的方法识别率相当,而在存储量和灵活性方面优势突出。

一种GTD模型参数估计的新方法

本文将能够精确描述高频电磁散射的几何绕射(GTD)模型取代传统的多重信号分类(MUSIC)算法所采用的指数和模型,并对MUSIC算法做了相应改进.提出利用特征分析方法的信号与噪声子空间正交特性,使改进后的MUSIC算法既能够精确估计目标散射中心位置,又能估计散射中心类型,取得了较好的效果.本文针对各种空间平滑预处理方法对噪声子空间与信号正交特性的影响进行了仿真,指出空间平滑预处理方法会影响噪声子空间及信号子空间结构,进而影响散射中心类型的估计,所以应对散射中心类型的最终结果进行修正.

基于MUSIC算法的GTD模型参数估计

将能够精确描述高频电磁散射的GTD信号模型引入MUSIC算法,并对MUSIC算法作了相应改进。利用特征分析方法的信号与噪声子空间的正交特性,使改进后的MUSIC算法既能精确估计目标散射中心位置,又能估计散射中心类型,取得了较好的效果。针对各种空间平滑预处理方法对噪声子空间与信号正交特性的影响进行了仿真,指出空间平滑预处理方法会影响噪声子空间及信号子空间结构,进而影响散射中心类型的估计,所以应对散射中心类型的最终结果进行修正。