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。     

Performance analysis of ordered CFAR detectors for MIMO radars

Ordered statistics is one of the proposed solutions to improve the detection in a multiple target environment. Some variants of this technique have been proposed for the SISO (Single Input Single Output) radars such as the OS-CFAR (Ordered Statistics CFAR), the GOSCA-CFAR (Generalized Ordered Statistics, Cell Averaging CFAR), the OSGO-CFAR (Ordered Statistics Greatest Of CFAR) and the OSSO-CFAR (Ordered Statistics Smallest Of CFAR) to deal with multiple target situations. In this paper, we generalize these CFAR detectors for the MIMO (Multi Input Multi Output) radars with three different schemes. We derive closed-form expressions of the probability of false alarm (Pfa) and the probability of detection (Pd) in a homogeneous environment for two schemes. The performance of these detectors for a non-homogeneous clutter environment (presence of interfering targets and clutter edge) has been assessed and compared. The results obtained showed that the best performance is obtained by the OSSO-CFAR.     

一种基于AD检验的CFAR检测器

传统的恒虚警率(CFAR)检测器鲁棒性较差,为此,提出一种基于AD检验的CFAR检测器AD-CA。通过Monte Carlo仿真得到AD检验的临界值,并删除异常样本。仿真结果表明,在均匀背景下,AD-CA的检测性能与OS检测器相当;在多目标背景下,AD-CA的检测性能比OS检测器有所提升,当干扰目标个数大于N–k时,仍能保持较好的检测性能。     

一种改进MVI-CFAR检测器的性能分析

VI-CFAR检测器在均匀环境和非均匀环境下均具有较强的自适应性,但若前后两个参考滑窗均存在干扰目标时,VI-CFAR检测性能急剧下降。为克服该问题,本文采用有序统计平均CFAR和有序统计选小CFAR来替代VI-CFAR中最小选择CFAR。所提出的算法不但解决了干扰目标位置随机分布的问题,还解决了当其中一个滑窗的干扰目标数超过最大容限时检测性能下降的问题。在参考滑窗中干扰目标个数与扰噪比均不相同的情况下,分析了统计量VI的变化,有助于评估和设计VI-CFAR类检测器在多目标环境下抗干扰目标的最大容限。仿真结果表明,该改进算法提高了VI-CFAR在多目标环境下的鲁棒性,与其他改进算法相比,其排序处理时间降低一倍。     

Matrix CFAR detectors based on symmetrized Kullback–Leibler and total Kullback–Leibler divergences

Target detection in clutter is a fundamental problem in radar signal processing. When the received radar signal contains only few pulses, it is difficult to achieve a satisfactory performance using the traditional detection algorithm. In recent times, a generalized constant false alarm rate (CFAR) detector on the Riemannian manifold of Hermitian positive-definite (HPD) matrix was proposed. The employment of this detector, which compares the Riemannian distance between the covariance matrix of the cell under test (CUT) and an average matrix of reference cells with a given threshold, has significantly improved the detection performance. However, the application of this detector in real scenarios is still limited by two problems; it is computationally expensive and the detection performance is not very good since the Riemannian distance is utilized. In this paper, the symmetrized Kullback–Leibler (sKL) and the total Kullback–Leibler (tKL) divergences, instead of the Riemannian distance, are used as dissimilarity measures in the matrix CFAR detector. According to sKL and tKL divergences, three average matrices, the sKL mean, the sKL median, and the tKL t center, are derived. Furthermore, the relationship between the detection performance and the anisotropy of the distance measure used in the matrix CFAR detector is explored. Numerical experiments and real radar sea clutter data are given to confirm the superiority of the proposed algorithms in terms of the computational complexity and the detection performance.     

一种新的基于模糊逻辑的CFAR检测器

基于模糊逻辑,无偏最小方差估计(UMVE)和单元平均(CA)提出一种新的恒虚警检测器(FUCAP)。它的前、后沿滑窗分别采用模糊UMVE方法和模糊CA方法得到映射到虚警空间的两个隶属函数值,再将这两个值相乘作为检测统计量。分析结果表明,FUCAP在均匀背景和多目标环境下均具有不错的检测性能。     

一种新的主动声纳模糊均值CFAR检测器

为了改善主动声纳的检测性能,本文研究了非高斯分布混响背景下采用模糊逻辑方法进行恒虚警检测设计的问题,提出了一种二元分布式模糊均值恒虚警检测器,两个子检测单元分别计算映射到虚警空间的隶属函数值,通过融合中心得到全局隶属函数值,实现背景混响功率水平估计,从而进行目标判决.仿真结果表明,基于代数和融合准则的检测器性能是最稳健的,相比传统的二进制“与”逻辑以及“或”逻辑,能够提供更好的检测效果.     

SAR图像CFAR检测的快速算法综述

针对合成孔径雷达(Synthetic aperture radar, SAR)图像目标检测中恒虚警率(Constant false alarm rate, CFAR)算法的广泛应用, 进行CFAR检测的快速算法分析具有重要研究价值. 首先概述了当前国内外对SAR图像CFAR检测快速算法的研究现状; 然后分别从快速预筛选和迭代计算方法两个方面对各类快速算法的实时性及性能进行了分析总结, 给出了四种基本CFAR检测器的迭代计算公式, 并提出了一种研究CFAR检测快速算法的基本框架, 现有的快速算法均可纳入该理论框架予以分析; 最后, 以经典双参数CFAR检测算法为例, 对该基本框架进行仿真实现和性能分析, 验证了其可行性与检测性能．结果表明: 新的CFAR检测快速算法基本框架充分融合了快速预筛选思想和迭代计算方法的优势, 有效提高了CFAR算法在SAR图像检测应用中的执行效率.     

Adaptive subspace detection of range-distributed target in compound-Gaussian clutter

This paper deals with high-resolution radar (HRR) adaptive detection of range-distributed target embedded in compound-Gaussian clutter which is modeled as a spherically invariant random process (SIRP). Using multiple dominant scattering (MDS) model of range-distributed target, we justify that range-distributed target can be modeled as a subspace random signal. The unknown deterministic parameters are replaced by their ML estimates and then the nonadaptive detector is proposed. A closed-form expression for the probability of false alarm of the nonadaptive detector is derived and it ensures CFAR property with respect to the unknown statistics of the clutter texture component. Moreover, an adaptive detector is obtained relying on a two-step GLRT-based design procedure. Performances of these proposed detectors are assessed through Monte Carlo simulations and are shown to have better detection performance compared with existing similar detector.     

一种基于ODV和CA的恒虚警检测器

基于有序数据方差(ODV)方法和单元平均(CA)方法提出一种新的恒虚警检测器(MODVCA),它的前沿和后沿滑窗分别采用ODV和CA产生局部估计,再取二者的和作为背景功率水平估计.在Swerling Ⅱ型目标假没下,推导出MODVCA在均匀背景下虚警概率的解析表达式,并与其他CFAR方法进行了比较,结果表明在均匀背景及多于扰目标情况下,MODVCA的性能均比MOSCA获得了改善,同时该检测器的样本排序时间只有ODV的四分之一.     

A versatile hardware architecture for a constant false alarm rate processor based on a linear insertion sorter

Constant False Alarm Rate (CFAR) algorithms are used in digital signal processing applications to extract targets from background in noisy environments. Some examples of applications are target detection in radar environments, image processing, medical engineering, power quality analysis, features detection in satellite images, Pseudo-Noise (PN) code detectors, among others. This paper presents a versatile hardware architecture that implements six variants of the CFAR algorithm based on linear and nonlinear operations for radar applications. Since some implemented CFAR algorithms require sorting the input samples, a linear sorter based on a First In First Out (FIFO) schema is used. The proposed architecture, known as CFAR processor, can be used as a specialized module or co-processor for Software Defined Radar (SDR) applications. The results of implementing the CFAR processor on a Field Programmable Gate Array (FPGA) are presented and discussed.     

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.     

基于CFAR海上溢油检测研究

在SAR图像处理的基础上,提出一种新的基于恒虚警率( CFAR-Constant False Alarm Rate) 技术,确定SAR 图像中检测溢油整体阈值的方法。该方法采用高斯分布(正态分布) 作为SAR 图像灰度的概率密度函数,由CFAR 技术直接导出用于检测海上溢油整体阈值的计算公式,进行虚警去除。该算法避免了复杂公式迭代和求解形状参数计算过程,也避免了用二分法寻找阈值的循环解算过程,提高了检测速度。使用ENVISAT图像对该算法进行检验,结果显示所提出的算法在检测精度和检测速度上都有明显的改进。     

一种自适应的合成孔径雷达图像目标检测方法

目标检测是自动目标识别的一个重要步骤,论文提出了一种自适应的SAR图像目标检测方法,该方法采用基于Weibull分布模型的恒虚警率(CFAR)检测技术,将参考窗口分块,判断各子块类型,根据各子块类型不同,自适应选择参考样本确定阈值。在检测过程中,利用灰度和方差特征,预先排除明显不为目标的像素。对CFAR检测结果,利用目标基本形状特征排除虚警。实验证明,该方法在同质区和非同质区背景下都具有较好的检测性能。     

Efficient resource allocation scheme for multi-service based on interference mitigation in LTE-Advanced networks

The exponential growth of various services demands the increased capacity of the next-generation broadband wireless access networks, which is toward the deployment of femtocell in macrocell network based on orthogonal frequency division multiple access. However, serious time-varying interference obstructs this macro/femto overlaid network to realize its true potential. In this article, we present a macro services guaranteed resource allocation scheme, which can mitigate various dominant interferences and provide multiple services in macro/femto overlaid Third-Generation Partnership Project Long Term Evolution-Advanced networks. We model our multiple services resource allocation scheme into a multiobjective optimization problem, which is a non-deterministic polynomial-time （NP）-hard problem. Then, we give a low-complexity algorithm consisting of two layers based on chordal graph. Simulation results verify that the proposed scheme can achieve better efficiency than the previous works and raise the satisfaction ratio of Guaranteed Bit Rate （GBR） services while improving the average performance of non-GBR services.     

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。     

一种新的基于UMVE和CA的恒虚警检测器

在雷达自动检测系统中,通常是将自动检测和恒虚警(CFAR)技术结合使用以保持在变化的杂波环境中获得可预测的检测性能和恒定虚警率.将无偏最小方差估计(UMVE)方法和单元平均(CA)方法结合,提出了一种新的恒虚警检测器(MUMCA-CFAR).采用UMVE和CA方法产生两个局部估计.再取二者的平均值作为背景噪声功率水平估计.在Swer-lingⅡ型目标假设下,推导出了MUMCA-CFAR在均匀背景下虚警概率和检测概率及多目标环境下检测概率的解析表达式.并与其它方法作了比较,结果表明该检测器在均匀背景和多目标环境下均具有相当优越的检测性能.     

一种合成孔径雷达图像阴影和目标检测的方法

提出了一种进行高分辨率SAR(synthetic aperture radar)图像阴影和目标检测的新思路.首先给出了基于等效视数模型的图像RCS(radar cross section)重构算法进行阴影检测,然后通过非线性积累,达到目标检测的目的.与CFAR(constant false alarm rate)检测相比,能够检测到阴影和更多感兴趣的目标.     

基于SAR图像目标检测的优化CFAR检测器算法研究

在目前的SAR目标检测中CFAR检测器被广泛的应用,而对相同或不同分辨率的图像,优化的CFAR检测器可以作为无偏检测。本文对优化的CFAR．检测器进行了研究。