Two adaptive detectors for range-spread targets in non-Gaussian clutter

This paper addresses adaptive detection of range-spread target in spherically invariant random vector clutter. Based on the nonadaptive detectors of NSDD-GLRT and SDD-GLRT, two adaptive detectors named ANSDD-GLRT and ASDD-GLRT are devised by replacing the unknown normalized clutter covariance matrix with the sample covariance matrix based on the secondary data. The formulas of detection probability and false alarm probability are deduced. Moreover, the constant false alarm rate properties of both ...     

稀疏距离扩展目标自适应检测及性能分析

在球不变随机向量杂波背景下,研究了稀疏距离扩展目标的自适应检测问题.基于有序检测理论, 利用协方差矩阵估计方法,分析了自适应检测器(Adaptive detector, AD).其中,基于采样协方差矩阵(Sample covariance matrix, SCM)和归一化采样协方差矩阵(Normalized sample covariance matrix, NSCM),分别建立了AD-SCM和AD-NSCM检测器.从恒虚警率特性和检测性能综合来看, AD-NSCM的性能优于AD-SCM和已有的修正广义似然比检测器.最后,通过仿真实验验证了所提方法的有效性.     

Generalized adaptive subspace detector for range-Doppler spread target with high resolution radar

A generalized adaptive subspace detector for range-Doppler spread target(RDST-GASD)in the non-Gaussian clutter is derived in this paper.The subspace model of multi-pulse wideband radar target returns is established in the frequency-slow time domain.The clutters are modeled as nonhomogeneous spherically invariant random vectors(SIRVs);that is,the power of the clutter is different from one range cell to another.The clutter covariance matrix is estimated with the secondary data.The constant false alarm rate(CF...     

Variable factorization model based on numerical optimization for hyperspectral anomaly detection

The objective of this article is to develop an anomaly detector as an analytical expression for detecting anomalous objects in remote sensing using hyperspectral imaging. Conventional anomaly detectors based on the subspace model have a parameter which is the dimension of the clutter subspace. The range of possible values for this parameter is typically large, resulting in a large number of images of detector output to be analyzed. An anomaly detector with a different parameter is proposed. The pixel of known random variables from a data cube is modeled as a linear transformation of a set of unknown random variables from the clutter subspace plus an error of unknown random variables in which the transformation matrix of constants is also unknown. The dimension of the clutter subspace for each spectral component of the pixel can vary, hence some elements in the transformation matrix are constrained to be zeros. The anomaly detector is the Mahalanobis distance of the resulting residual. The experimental results which are obtained by implementing the anomaly detector as a global anomaly detector in unsupervised mode with background statistics computed from hyperspectral data cubes with wavelengths in the visible and near-infrared range show that the parameter in the anomaly detector has a significantly reduced number of possible values in comparison with conventional anomaly detectors.     

Knowledge-based target detection in compound Gaussian clutter with inverse Gaussian texture

This paper addresses the problem of adaptive detection of radar targets embedded in heterogeneous compound-Gaussian clutter environments. Based on the Bayesian theory, a priori knowledge of clutter is utilized to improve detection performance. The clutter texture is modeled by the inverse Gaussian distribution to describe the heavy-tailed clutter. Furthermore, clutter's heterogeneity results in insufficient secondary data, and the inverse complex Wishart distribution is exploited to model the speckle covariance matrix. Based on a priori distributions of clutter, a novel detector without using secondary data is derived via the generalized likelihood ratio test (GLRT). Monte Carlo experiments are performed to evaluate the detection performance of the proposed detector. Experimental results illustrate that the proposed detector outperforms its competitors in scenarios with limited secondary data.     

Persymmetric adaptive subspace detectors for range-spread targets

This paper investigates the problem of adaptive detection of a range-spread target in colored Gaussian disturbance. The range-spread target is described by a multi-rank subspace model, which lies in a subspace but with unknown coordinates. The disturbance, usually including clutter and thermal noise, has an unknown covariance matrix. Under the above assumption, we design the Rao and generalized likelihood ratio test (GLRT) detectors by the two-step procedure, which incorporates persymmetric structure of received data. The two detectors are shown to coincide with each other. Remarkably, the proposed detector ensures constant false alarm rate property. Experimental results conducted by both simulation and real data verify that the proposed detector outperforms the existing counterparts in training-limited scenarios.     

利用高斯混合模型的SAR图像目标CFAR检测新方法

SAR（合成孔径雷达）图像杂波分布模型种类繁多且对实际地物的建模能力有限。在使用基于杂波统计模型的CFAR(恒虚警率)算法对SAR图像进行目标检测时,杂波统计模型的失配会导致检测结果产生较大的CFAR损失,算法精度不高。提出了一种基于高斯混合模型的CFAR检测新方法。该方法以理论上可以拟合任意形状概率密度分布的高斯混合模型对实际SAR图像的背景杂波进行拟合,利用拟合后得到的分布模型,根据CFAR检测的原理推导出目标检测阈值的计算公式完成目标的检测。新方法对服从不同分布模型的背景杂波,使用形式上统一的模型进行描述,克服了CFAR检测高度依赖背景杂波分布的缺点,提高了CFAR的通用性。实验结果表明,即使在背景杂波类型未知的情况下,新方法依然得到了良好的目标检测效果。     

Adaptive detection of range-spread targets in compound Gaussian clutter with the square root of inverse Gaussian texture

The compound Gaussian clutter with the square root of inverse Gaussian texture component has been successfully used for modeling the heavy-tailed non-Gaussian clutter measured by high-resolution radars. In high-resolution radars, the targets may extend along multiple consecutive range cells, which are called range-spread targets. In this paper, we consider the range-spread target detection problem in the compound Gaussian clutter with the square root of inverse Gaussian texture. Three adaptive detectors are proposed based on Bayesian one-step generalized likelihood ratio test, maximum a posteriori generalized likelihood ratio test and Bayesian two-step generalized likelihood ratio test, respectively. Finally, the detection performances of the proposed detectors are evaluated by the Monte Carlo simulation. The simulation results show that the proposed detectors have better detection performance of range-spread target than the conventional generalized likelihood ratio test detector.     

一种多目标环境下的SAR图像自适应CFAR检测方法

多数CFAR检测器在多目标检测环境下需要关于干扰目标的先验信息,当检测环境发生变化时,这些检测器很难维持稳定的检测性能。针对多目标环境下的SAR图像目标检测,提出一种新的自适应CFAR（恒虚警）检测器。该检测器利用局部的杂波功率水平估计以及目标和杂波的方差特征筛选出参考窗中的均匀杂波像素,同时剔除掉干扰目标像素;在筛选过程中,每一步使用的判决门限根据上一步的判决结果自动更新;最后对筛选出的样本点作单元平均处理形成检验统计量;完全不需要干扰目标的任何先验信息。利用实测数据仿真研究了该检测器的检测性能与运行效率,实验结果表明,相对单元平均CFAR检测器及有序统计量CFAR检测器,该检测器提高了检测性能,保留了目标精细的结构特征,而运行效率与有序统计量CFAR检测器相当,很具实用性。     

群目标联合检测与估计误差界的递推形式

在随机有限集框架下给出了当杂波和漏检存在时,群目标联合检 测与估计(Joint detection and estimation, JDE)误差界的递推形式. 首先,将多个群目标运动过程建模为一个多Bernoulli过程, 并采用连续个体目标数假设建模群目标观测似然函数; 其次,采用最优子模式 分配距离定义群目标JDE误差; 最终,利用信息不等式推导获得了建议的误差界. 仿真实验在不同杂波密度和检测概率场景下利用群势概率假设密度 和群势平衡多目标多Bernoulli滤波器对该误差界的有效性进行了验证.     

机载雷达级联降维空时自适应杂波抑制方法

提出一种机载雷达杂波抑制的级联降维空时自适应算法,即,先对全空时两维接收数据进行预滤波处理,将杂波局域化,降低杂波自由度;然后对预处理输出的信号的相关矩阵进行子阵划分,求解低维权向量,进一步降低运算量和采样要求。理论分析和实验仿真结果表明,所提算法具有良好的收敛性能和杂波抑制能力,并且对于阵元随机幅相误差和杂波起伏具有很好的容差能力。基于实测数据的实验验证了算法的有效性和稳健性。     

Sea surface target detection based on complex ARMA-GARCH processes

In financial applications, it is common practice to fit return series by AutoRegressive Moving-Average (ARMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. In this paper, we develop a complex-valued ARMA-GARCH model for the sea clutter modeling application. Compared with the AR-GARCH model, the additionally introduced MA terms make the proposed model capable of considering the dependence of conditional variances of adjacent echo measurements as model coefficients, improving the modeling precision by taking advantage of the strong correlations between adjacent measurements. Based on the complex-valued ARMA-GARCH process for sea clutter modeling, we further develop a sea surface target detection algorithm. By analyzing a large number of the practical sea clutter data, we evaluate its performance and show that the proposed sea surface target detector offers a noticeable improvement for the probability of detection, comparing with the state-of-the-art AR-GARCH detector.     

An adaptive cell averaging-based CFAR detector for interfering targets and clutter-edge situations

In practice, there are two common situations when the independent and identically distributed (IID) assumption no longer holds: (i) there is a clutter edge and (ii) there is an outlier, e.g., a clutter spike, an impulsive interference, or another interfering target. These can result in masking of weaker targets near stronger ones and excessive false alarms at clutter edge transitions. In this paper, a new constant false alarm (CFAR) detector is proposed, which uses a goodness of fit test to verify the IID assumption. If it is decided that the data in the reference window is IID, the cell averaging (CA)-detector is applied. Otherwise, a range-heterogeneous detection algorithm is applied to provide homogeneous samples to develop a CA-based detector. The performance study shows that the proposed detector performs like the CA detector in the homogeneous situation and outperforms other competing CFAR detectors in heterogeneous situations caused by multiple targets and clutter edge.     

一种基于椭圆RHM的扩展目标Gamma高斯混合CPHD滤波器

针对杂波环境下扩展目标形状难以估计、目标跟踪精度低等问题, 提出一种自适应估计扩展目标形状的伽玛高斯混合势概率假设密度算法(GGM-CPHD). 该算法将目标的扩展形状建模为椭圆随机超曲面模型, 并将其嵌入到GGM-CPHD 滤波器中, 更新扩展目标的质心、椭圆形状和方向等信息以完成对扩展目标的跟踪. 通过杂波环境下未知数目的扩展目标仿真实验, 表明了所提出算法在质心状态和椭圆长短轴的估计精度方面要优于传统的基于随机矩阵的伽玛高斯逆韦氏CPHD滤波器.

     

基于监督非相干字典学习的极化SAR图像舰船目标检测

提出了一种结构化非相干字典学习算法 (Structured incoherent dictionary learning, SIDL),并将该方法应用于极化SAR (Polarimetric synthetic aperture radar, PoLSAR)图像舰船目标检测. 在字典学习阶段,构建了一个新的目标函数,为了降低子字典对交叉样本的稀疏表示能力, 将子字典对交叉样本的重构能量约束及子字典互相干性约束加入到字典学习目标函数中. 通过这两个约束, 降低了子字典对交叉样本的表示能力,目标和杂波的极化特征矢量在学习获得的字典下具有良好的区分特性. 该方法不依赖于目标后向散射能量,只利用学习获得的极化字典,根据测试样本在极化字典下的稀疏表示进行目标的检测. 实验采用RADARSAT-2数据进行了验证,对比实验结果表明,本文提出的方法可以更好地抑制杂波,对弱小目标实现检测,获得了更好的检测效果.     

基于子空间和直方图的多记忆自适应相关滤波目标跟踪算法

为了增强相关滤波算法(CF)在目标遮挡或背景干扰情况下跟踪的鲁棒性,提出基于子空间和直方图的多记忆自适应相关滤波目标跟踪算法.首先,针对CF使用的模板单一无法应对不同时期相邻帧目标表现的差异,提出利用随机更新策略学习多个目标模板,应对不同时期的目标变化.然后,针对不同的更新模板得到多个候选目标,利用子空间学习上一帧的表示系数,综合判断候选目标的准确性.同时,因为CF与子空间表示均利用模板判断跟踪结果,对背景杂乱等情况判断容易造成偏差,所以引入颜色直方图,利用统计特征作为独立的判断依据,增强算法对候选目标判断结果的准确性.在标准视频集上的实验表明,文中算法具备一定的抗遮挡及抗背景干扰能力.     

联合形态滤波和MRF模型的红外小目标检测

针对复杂背景下红外弱小目标检测难题,提出一种基于自适应形态滤波和Markov随机场(MRF)模型的小目标检测算法。设计基于图像局部熵优化的自适应形态滤波器,采用该滤波器进行背景杂波抑制和目标增强,利用MRF理论描述图像像素间关系,构造新的势函数和能量函数,建立目标检测识别模型,通过模型计算自动识别出红外图像中的小目标。理论分析和实验结果表明,该算法可在复杂背景下自适应地抑制背景杂波,成功检测出红外小目标。     

两种非参量检测器在非瑞利杂波中的检测性能

现代高分辨率雷达系统中,杂波分布已不再简单地服从瑞利分布,其统计特性往往无法预先确定,此时针对性较强的参量检测方法就失去了恒虚警的检测能力,因此鲁棒性较强的非参量检测方法已成为一个重要的研究方向.文中针对非瑞利杂波中广义符号(GS)检测器和Mann-Whitney(MW)检测器两种非参量检测器在两种非瑞利杂波中的检测性能进行了仿真分析.选择韦伯(Weibull)分布和对数正态(log-normal)分布为非瑞利杂波模型,详细给出了仿真模拟框图,采用Monte Carlo仿真方法,分别得出了GS、MW及最佳线性参量检测器在Weibull和log-normal杂波对非起伏目标的检测性能曲线.仿真结果表明,GS和MW在非瑞利杂波中的检测性能均优于最佳线性参量检测器,不同的杂波分布具有相同的均值与中值比(ρ)时,两种检测器性能相差不大.论证了增大独立脉冲积累数(M)是提高检测性能的有效手段.     

未知杂波环境下的多目标跟踪算法

提出了一种未知杂波环境下的多目标跟踪算法. 该算法通过有限混合模型(Finite mixtrue model, FMM)建立多目标似然函数, 其中混合模型参数可通过期望极大化(Expectation maximum, EM)算法及模型合并与删除技术得到. 由估计的混合模型参数可进一步得到杂波模型估计、目标个数估计以及多目标状态估计. 类似基于随机有限集(Random finite set, RFS)的多目标跟踪算法, 该算法也可避免目标与测量的关联过程. 仿真实验表明, 当杂波分布未知并且较复杂时, 本文算法的估计效果要明显优于未进行杂波拟合时的多目标跟踪算法.     

海面目标的自适应预白化检测前跟踪算法

针对超视距雷达的海面目标检测问题,提出一种基于自适应预白化处理的检测前跟踪（TBD）算法。在目标TBD处理之前,利用海杂波的自回归模型构建白化滤波器进行杂波预白化,在跟踪阶段采用递归贝叶斯算法估计目标运动状态,在检测阶段通过跟踪滤波器的输出构造广义似然比进行似然比检测。不同信噪比下的仿真结果表明,该算法能有效抑制海杂波,检测到低信噪比的目标。．