基于Fisher分布的极化合成孔径雷达统计建模及其参数估计方法

为更准确地描述高分辨极化合成孔径雷达(Synthetic Aperture Radar, SAR)图像的尖峰和长拖尾等统计分布特性, 提出了基于Fisher分布的极化图像多变量乘积模型, 并研究了其参数估计方法.首先给出了柯西分布相干斑噪声等效纹理分量的概率密度函数及其低阶矩特征;然后利用散射因子服从F分布的等效纹理变量与高斯散斑变量相乘形成的多变量乘积统计模型, 得到了Fisher分布模型的概率密度函数, 并推导了其多视协方差矩阵的概率密度函数和矩阵行列式值的低阶矩特征;最后提出了基于矩阵行列式值的矩估计和基于Mellin变换的对数累积量估计等两种参数估计方法, 并进行了对比, 同时通过仿真数据和实测数据验证了理论模型和新参数估计方法的有效性.这为高分辨极化SAR图像建模、目标检测和识别等领域的理论研究和工程实现提供了新途径.     

一种度量目标散射随机性的新参数

针对现有目标散射随机性度量参数运算量偏大、度量不合理等问题,本文提出了一种基于目标散射回波极化度的度量目标散射随机性的新参数——散射随机系数。在分析散射回波极化度与散射随机性之间的相互关系的基础上,首先给出了基于散射回波极化度的散射随机系数定义。然后通过分析新参数随着目标各向异性和散射阶次的变化情况,阐明新参数在度量目标散射随机性方面是合理的。最后结合San Francisco和Flevoland两地区的实测极化数据验证了新参数在度量目标散射随机性方面的有效性,与极化散射熵的对比实验结果表明了在大数据量的POLSAR图像散射分类应用中新参数运算效率更高。      

时变电磁波瞬态极化投影矢量的数字特征

瞬态极化概念是描述时变电磁波极化动态变化特性的重要手段.在正态假设下,研究了时变电磁波瞬态极化投影矢量(IPPV)的概率分布和均值、方差、混合二阶矩等数字特征,得到了电磁波IPPV一些重要统计性质.分析了JPPV相对于Stokes参数表征的优势.证明了电磁波在特征极化基上IPPV的3个分量互不相关,得到了IPPV的数字特征随波的极化度的变化关系.这些工作为随机性散射波或辐射波的极化特性分析提供了有用工具,对雷达目标动态极化散射特性表征、目标检测、目标特征提取与识别等领域的研究具有启发意义.     

基于Freeman分解的植被参数反演新方法

基于极化Freeman分解的极化协方差矩阵参数,极化干涉SAR互协方差矩阵可简化建模为植被高度、消光系数和地面干涉相位的函数。基于此,该文建立了以极化干涉SAR互协方差矩阵估算值与互协方差矩阵观测值之差为目标函数、以3个植被参数为未知量的优化模型,提出了基于Freeman分解的植被参数反演新方法。该方法避免了三阶段植被参数估计方法所面临的体相关系数确定问题,提供了一种独立于三阶段植被参数估计的新思路。仿真结果验证了新方法的有效性。     

基于球形截断协方差矩阵的极化SAR分布式目标稳健定标方法

常规的基于分布式目标的定标算法通过假设协方差矩阵满足特定形式,并用样本协方差矩阵来估计失真参数。然而,样本协方差矩阵并非稳定的协方差矩阵估计子。尤其是当场景中包含不满足定标算法要求的目标时,样本协方差矩阵会偏离理想形式,以致失真参数估计精确度下降。球形截断协方差矩阵方法能够有效地抑制离群样本对协方差矩阵估计的影响,该文将其引入到极化SAR的定标中,并对其可行性进行了理论分析。最后,利用机载全极化SAR数据,验证了该方法能够有效地降低失真参数估计的不确定度,从而提高失真参数估计的稳健性。      

多极化数据空时协方差矩阵融合方法

空时自适应处理(STAP)方法具有良好的地面动目标检测性能,然而在非均匀场景下,STAP常由于训练样本数不足而存在性能损失。建立了极化空时三维信号模型,指出不同极化通道间杂波功率存在差异而空时二维结构在理论上一致,并在此基础上提出最大似然参数估计的多极化杂波协方差矩阵融合方法以改善样本数不足导致的协方差矩阵估计精度较差问题;对杂噪比较大且各极化通道间的空时协方差矩阵结构误差较小的特殊情况,通过对杂波协方差矩阵以及信杂噪比损失的分析指出平均融合即具有良好的检测效果。仿真结果验证了所提方法的有效性。     

一种基于表面散射相似度的SAR图像极化增强方法

针对传统极化增强在多类目标增强和极化信息保持方面存在的不足,该文基于表面散射的相似度提出了一种新的极化增强方法。该方法根据目标表面散射的相似度,对不同散射类型的目标相干矩阵乘以不同实系数,从而增大了表面散射的目标与其它散射的目标的对比差异。同时由于该方法将表征目标的相干矩阵作为整体进行运算,因而保持了目标的极化信息。最后利用NASA SIR-C/X-SAR实测极化数据验证了该方法的有效性。     

雷达目标极化散射特性的表征方法

归纳了现有雷达目标极化散射特性的表征方法,回顾了雷达目标极化散射经典表征的物理概念及内涵。阐述和深化了在时变极化波激励下雷达目标的瞬态极化表征方法,提出了瞬态极化相似系数和瞬态极化相似度等概念。简要介绍了雷达目标的瞬态极化时频分布表征思路。试图理清这些刻画手段的研究思路,为适应不同的应用需求提供理论支持。     

干扰条件下极化雷达目标检测性能分析

推导了受干扰目标的极化协方差矩阵及其统计特性,建立了干扰条件下最优极化检测器的数学模型,在此基础上以水面舰船和装甲车辆为例分析了不同干扰功率与干扰机极化配置方式对极化雷达目标检测性能力的影响,揭示了利用压制性干扰降低极化雷达目标检测性能的最佳干扰功率需求。     

基于张量四元数极化平滑的极化-DOA估计

为了解决相干信号的极化平滑算法在小快拍数和低信噪比条件下估计性能较差的问题,结合四元数的正交特性和协方差张量方法,提出了一种基于张量四元数的极化平滑多重信号分类（Multiple Signal Classification,MUSIC）解相干算法。首先,为了充分利用接收数据样本中的多维结构信息,建立了由张量四元数表示的柱面共形阵列极化平滑信号模型;其次,将平滑后的张量协方差矩阵通过高阶奇异值分解得到信号子空间;最后,通过极化秩亏MUSIC算法对入射相干信号分别进行二维波达方向（Direction of Arrival,DOA）估计和极化参数估计。仿真结果表明,该算法在小快拍数和低信噪比条件下具有更高的估计精度和分辨能力。     

Estimation of the Equivalent Number of Looks in Polarimetric Synthetic Aperture Radar Imagery

This paper addresses estimation of the equivalent number of looks (ENL), an important parameter in statistical modeling of multilook synthetic aperture radar (SAR) images. Two new ENL estimators are discovered by looking at certain moments of the multilook polarimetric covariance matrix, which is commonly used to represent multilook polarimetric SAR (PolSAR) data, and assuming that the covariance matrix is complex Wishart distributed. First, a second-order trace moment provides a polarimetric extension of the ENL definition and also a matrix-variate version of the conventional ENL estimator. The second estimator is obtained from the log-determinant matrix moment and is also shown to be the maximum likelihood estimator under the Wishart model. It proves to have much lower variance than any other known ENL estimator, whether applied to single-polarization or PolSAR data. Moreover, this estimator is less affected by texture and thus provides more accurate results than other estimators should the assumption of Gaussian statistics for the complex scattering coefficients be violated. These are the first known estimators to use the full covariance matrix as input, rather than individual intensity channels, and therefore to utilize all the statistical information available. We finally demonstrate how an ENL estimate can be computed automatically from the empirical density of small sample estimates calculated over a whole scene. We show that this method is more robust than procedures where the estimate is calculated in a manually selected region of interest.     

非均匀环境中的分布目标自适应检测

该文研究了非均匀环境中的分布目标和多点目标的检测。其中,假设辅数据协方差矩阵服从以主数据协方差矩阵为条件的逆Wishart分布,且均值与之成比例。首先给出主数据协方差矩阵、比例因子和目标幅度的最大似然估计(MLE),然后基于贝叶斯理论和广义似然比(GLRT)判决准则提出了一种检测器。当目标只存在单个距离门时,检测器和自适应相干估计器(ACE)一致;当目标跨越多个距离门时,检测器和广义自适应子空间检测器(GASD)一致。但不同在于ACE和GASD都是基于未知的确定干扰协方差矩阵的。另外,该检测器具有恒虚警率(CFAR)特性,并且有很好的检测性能。     

Optimal speckle reduction for the product model in multilookpolarimetric SAR imagery and the Wishart distribution

Several optimal techniques exist to reduce speckle effects on polarimetric data, e.g. the Linear Minimum Mean Square Error (LMMSE) vector filter for multilook detected data or optimum summations such as the Polarimetric Whitening Filter (PWF) for one look complex data. Among other drawbacks, these standard methods do not preserve full polarimetric data, or they do not use the a priori texture distribution, or they are restricted to one look data. In the simplified case of data satisfying the so-called “product model”, new optimal techniques are described in this paper that are able to reduce speckle effects on multilook data, while preserving fully polarimetric information and texture variations. This “product model” is valid when the scene texture has a large scale of variation and is polarization independent, for instance in K-distributed clutter. Under this assumption, the measured covariance matrix (multilook data) is the product of a scalar random variable μ (the texture) and the covariance matrix C_zh of an equivalent Gaussian homogeneous surface. C_zh is the mean covariance matrix and contains the polarimetric information. A PWF for multilook complex data (MPWF) is proposed and is shown to be related to optimal statistical estimators of the texture (Maximum Likelihood, Maximum A Posteriori, MMSE…) when the complex Wishart distribution is used. The ML estimation of C _zh for textured areas is given and the adaptive filters based on these new tools are described. The results indicate a large speckle reduction. Moreover, the mean values of polarimetric features such as the magnitude and the phase of the HH-VV complex degree of coherence are preserved     

A covariance matrix shrinkage method with Toeplitz rectified target for DOA estimation under the uniform linear array

A covariance matrix shrinkage method is proposed to make an improvement of the direction of arrival (DOA) estimation under a uniform linear array in a scenario where the number of sensors is large and the sample size is relatively small. The main contribution is that we provide a shrinkage target with Toeplitz structure and deduce a closed-form estimation of the shrinkage coefficient. The closed-form and the expectation of the shrinkage coefficient estimate are calculated based on the unbiased and consistent estimates of the trace and moments of a Wishart distributed covariance matrix. The statistical property of the shrinkage coefficient estimate is discussed through theoretical analysis and simulations, which demonstrate the shrinkage coefficient estimate can ensure that the proposed covariance matrix estimate is a good compromise between the sample covariance matrix (SCM) and the target. The root-mean-square-error (RMSE) simulations of DOA estimation show that the proposed method can improve the multiple signal classification (MUSIC) DOA estimation performance in the case of low signal-to-noise ratio (SNR) with small sample size, and also can provide a satisfactory performance at high SNR.     

不需要训练数据的分布式MIMO雷达距离扩展目标检测器

在非均匀杂波环境中,研究了高分辨率分布式多输入多输出(MIMO)雷达的距离扩展目标检测问题。由于分布式MIMO雷达观测到的杂波是非均匀的,无法获得足够的独立同分布的均匀训练数据来估计检测单元的杂波协方差矩阵。采用复逆Wishart分布对杂波协方差矩阵建模,基于该杂波模型设计了一种不需要训练数据的分布式MIMO雷达距离扩展目标广义似然比检测器。数值仿真结果表明:在非均匀杂波环境中,所设计的检测器的性能比用训练数据的协方差矩阵类检测器有明显的改进。     

The Rao and Wald Tests Designed for Distributed Targets with Polarization MIMO Radar in Compound-Gaussian Clutter

In high resolution radars, the distributed target is usually modeled as a few isolated points referred to multiple dominant scattering centers, while the clutter is a compound-Gaussian model. Additionally, the polarimetric diversity can be exploited to enhance detection performance. Motivated by extending the detection problem of multiple-input multiple-output (MIMO) radar to such cases, this paper mainly addresses distributed targets detection problem with polarization MIMO radar against a compound-Gaussian clutter dominated scenario with unknown covariance matrix. The adaptive detectors based on Rao and Wald criteria are studied, and a two-step design procedure is adopted. Specifically, the Rao and Wald tests are derived by assuming a known covariance matrix, and then a suitable estimation of the covariance matrix based on the secondary data is inserted into the derived detectors to make them fully adaptive. Some numerical results are presented together with a polarization generalized likelihood ratio test (GLRT), showing that the derived detectors provide excellent detection performance in spiky clutter for distributed targets, and that the polarimetric diversity can be exploited to improve detection performance. Overall, the Wald test performs the best.     

基于贝叶斯的高斯杂波背景下MIMO雷达自适应检测算法

对于集中式多输入多输出(Multiple-Input Multiple-Output, MIMO)雷达,该文研究了高斯杂波背景下的目标检测问题。该文假设杂波的协方差矩阵是未知随机的,且服从逆复Wishart分布,基于贝叶斯方法和广义似然比检验准则设计了两种新型自适应检测器。该文提出的贝叶斯检测器具有两个显著的优点:(1)不需要训练数据;(2)杂波的先验知识体现在设计方案中,从而提高了检测性能。仿真结果显示该文提出的贝叶斯检测器的检测性能优于目前常用的非贝叶斯检测器,特别是在发射波形采样数较少时。另外,该贝叶斯检测器在参数失配条件下的性能会有一定程度下降。      

Quantitative comparison of classification capability: fullypolarimetric versus dual and single-polarization SAR

Addresses the land-use classification capabilities of fully polarimetric synthetic aperture radar (SAR) versus dual-polarization and single-polarization SAR for P-, L-, and C-band frequencies. A variety of polarization combinations are investigated for application to crop and tree age classification. Based on the complex Wishart distribution for the covariance matrix, maximum likelihood classifiers for all polarization combinations are used to assess quantitative classification accuracy. Thus, this allows optimally selecting the frequency and the combination of polarizations for various applications     

一种度量目标散射相似性的新参数

针对目标相似性参数度量分布式目标与典型目标散射相似程度不准确的问题,本文利用规范散射Pauli基矢量和分布式目标相干矩阵,定义了一个度量目标散射相似性程度的新参数--目标散射相似性.由于该参数综合考虑了分布式目标主散射机制、次要散射机制与规范散射的相似性,以及不同散射机制对廊的发生概率,因而它能准确反映分布式目标与规范目标散射的平均近似程度.与相似性参数一样,新参数具有目标旋转不变性、尺度无关性等特性.作为一种实际应用,给出了分布式目标与金属球、角反射器等典型目标的散射相似性.结合旧金山地区AIRSAR极化数据,讨论了这些相似性测度在地物分类方面的应用.