单元筛选后作最大选择的CFAR自适应检测器

提出一种新的CFAR自适应检测方法，对这种新的检测方法在斯威林2型目标的假设下进行了检测性能的分析，得到了在均匀背景和强干扰目标存在的情况下的虚警概率和检测概率，并把它同CA、GO、SO等检测方法进行了比较。分析结果表明：这种方法在均匀背景和非均匀背景情况下都有很好的检测性能。     

MIMO雷达中单元平均恒虚警检测性能分析

针对高斯杂波背景,导出了MIMO雷达中CA-CFAR的虚警概率与检测概率的解析表达式,分析了CA-CFAR检测器在均匀杂波、多目标干扰及杂波边缘三种典型杂波背景中的检测性能。结果表明,CA-CFAR检测器在均匀杂波中性能较好,但在多目标干扰环境中性能严重下降。     

OSGO-和OSSO-CFAR在K分布杂波背景下的性能分析

该文证明了形状因子已知条件下OSGO-CFAR和OSSO-CFAR检测器在均匀统计独立的K分布杂波背景下具有恒虚警性能,分析了两种检测器在均匀杂波背景、杂波边缘和存在强干扰目标情况下的检测性能。并与OS-CFAR进行了比较,结果表明OSGO-CFAR在均匀杂波背景和存在强干扰目标情况下带来的附加检测损失很小, 在杂波边缘具有更好的虚警控制能力。所以,OSGO-CFAR是K分布杂波背景下一种性能比较好的恒虚警检测器。     

一种新的最大选择恒虚警检测器

本文提出一种基于有序统计(OS)和单元平均(CA)产生局部估计,并应用最大选择(GO)产生检测单元杂波功率水平估计Z的新的恒虚警检测器(OSCAGO)。我们推导出了该检测器在Swerling Ⅱ型目标假设下的虚警概率(Pfa)、检测概率(Pd)和度量平均判决门限(ADT)解析表达式。分析了它在均匀背景和强干扰环境中的检测性能。并且把它与OS-,GOSGO-CFAR进行了比较。结果表明,OSCAGO在均匀杂波背景和多目标情况下的检测性能与OS和GOSGO相比,都有很明显的提高。在干扰目标数为某些值时,OSCAGO的CFAR损失比GOSGO小近3dB。     

恒虚警极化自适应子空间检测器

本文研究了在未知统计特性的局部均匀高斯杂波环境中的相干雷达极化自适应目标检测问题.基于广义似然比检验提出极化自适应子空间检测器,并推导出虚警概率和检测概率的理论表达式.研究结果证明该算法在均匀杂波环境和局部均匀杂波环境都有恒虚警的性质.比较了均匀杂波环境下极化自适应子空间检测器、极化广义似然比检验和极化自适应匹配滤波器算法的检测性能,仿真分析了不同极化状态局部均匀杂波环境下极化自适应子空间检测器算法在的检测性能.     

一种基于有序统计的MIMO雷达CFAR检测器

针对多输入多输出(MIMO)雷达的体制特点,提出了一种基于有序统计的MIMO雷达CFAR检测器(LCIOSOS-CFAR),给出了虚警概率与检测概率的表达式;然后在各种杂波背景下对检测器性能进行了仿真分析,并与经典的CA-CFAR检测器进行比较.仿真结果表明,LCIOSOS-CFAR检测器在均匀杂波背景下较CA-CFAR有较小损失,在多目标干扰环境下较CA-CFAR性能改善明显,在杂波边缘背景下虚警峰值小于CA-CFAR,实际应用中具有较强的鲁棒性.文中还分析了序值选取对LCIOSOS-CFAR检测性能的影响.     

全卷积神经网络应用于SAR目标检测

在合成孔径雷达（SAR）图像目标检测中,由于场景杂波的复杂多变,对背景杂波统计模型估计难度增加,从而导致多数检测器容易受到背景杂波的干扰。针对如何避免场景杂波对目标检测干扰的问题,提出了一种基于全卷积神经网络的SAR目标检测模型。该模型将目标检测任务转化为像素分类问题,利用卷积神经网络对数据集中目标像素特征和背景杂波像素的先验信息进行自主学习,有效减少了虚警目标的数量;通过对目标及其阴影区域的联合检测,提高了目标的检测概率。对多个不同场景图像进行测试,实验结果表明提出的检测模型具有良好的检测性能和鲁棒性能,与传统恒虚警检测算法相比,在无需考虑背景杂波统计模型前提下有效降低了虚警概率。     

基于准最佳加权有序统计的最大选择CFAR检测算法

为了提高恒虚警检测器在均匀背景中的检测性能及增强对干扰的鲁棒性，本文提出了一种准最佳加权（ＱＢＷ）有序统计方法．基于这种方法，还提出了准最佳加权最大选择恒虚警检测器（ＱＢＷＧＯ－ＣＦＡＲ），它的前、后沿滑窗均采用ＱＢＷ方法来产生局部估计，将局部估计中的最大值作为检测器对杂波功率水平的估计，设置自适应检测门限，应用文献［３］提出的自动筛选技术在ＳｗｅｒｌｉｎｇⅡ型目标及瑞利杂波假设下，推导出了它的Ｐｆａ、Ｐｄ、ＡＤＴ及杂波边缘虚警尖峰的数学解析表达式分析结果表明，它在均匀背景及多目标和杂波边缘引起的非均匀背景中的性能，均比ＧＯＳＧＯ或ＯＳＧＯ获得了改善．在特殊情况下，ＱＢＷＧＯ退化为ＧＯ和ＭＸ－ＣＭＬＤ     

一种基于拟合优度检验的恒虚警检测方法

针对传统的基于滑窗自适应门限恒虚警检测方法在非高斯环境下和多目标干扰环境下,性能下降的问题,提出了一种基于拟合优度检验的恒虚警检测方案,该方法利用了目标回波与背景杂波统计特性的差异,通过检验待检单元的回波样本是否服从背景分布来检测目标:如果待检单元样本服从背景分布,则有理由相信待检单元回波源于背景杂波,从而判断没有目标存在;否则,将判断有目标存在.和传统的基于自适应门限的检测方法相比,该方法受背景分布特性和干扰目标的影响很小.仿真实验表明,在尖锐的非高斯杂波环境下以及多目标干扰环境下,都能保持更优的检测性能.     

K—分布杂波的顺序统计恒虚警性能分析

研究 Ｋ—分布杂波背景下的顺序统计恒虚警检测器的性能,分析检测器在均匀统计独立杂波背景下的性能,给出了理论门限系数值及其仿真的门限系数值,并讨论相关杂波的恒虚警性能。分析了检测器在杂波的边缘和存在多目标及干扰时的目标检测性能。从仿真结果分析得出, Ｏ Ｓ— Ｃ Ｆ Ａ Ｒ 检测器在杂波边缘及多目标情况下具有比单元平均恒虚警（ Ｃ Ａ— Ｃ Ｆ Ａ Ｒ）好得多的性能,特别是在参考单元比较少的情况下。     

高分辨SAR图像自动区域筛选目标检测算法

在G0分布背景杂波假设下,基于VI-CFAR算法该文提出一种自动区域筛选的恒虚警目标检测算法,以解决高分辨SAR图像复杂环境背景下的目标检测问题。该算法首先利用变化指数(VI)统计量对局部参考窗内的均匀区域进行筛选,以剔除参考窗内具有目标干扰点的非均匀区域;然后利用均值比(MR)统计量对参考窗内同质的均匀区域进行区域合并,以解决杂波边界处的背景杂波筛选问题;最后利用筛选到的同质均匀区域内的像素集合进行背景杂波参数估计,对待检测区域实现二值检测。通过实测SAR图像车辆目标检测实验表明,在多目标和杂波边界复杂环境背景下,该算法具有较稳定的检测性能和虚警抑制能力。     

A NEW CFAR DETECTOR WITH GREATEST OPTION

This paper presents a new CFAR detector based on Ordered Statistics (OS) and Cell-Averaging (CA) forming local estimates, and using Greatest-Of selection (GO) to form clutter power level estimate Z in test cell(OSCAGO). Under the Swerling II assumption, the analytic expressions of Pfa,Pd and ADT of this detector are derived, its detection performance in homogeneous background and in strong interfering targets environment are analyzed and compared it with OS, GOSGO detectors. The results show that the detection performance of OSCAGO in homogeneous background and in multiple-target situations are obviously better than those of OS and GOSGO. When the number of interfering targets is equal to certain value, the CFAR loss of OSCAGO is about 3dB less than that of GOSGO.     

基于有序数据可变索引的SAR图像目标检测方法

该文提出了一种基于有序数据可变索引(Ordered Data Variability Index, ODVI)的SAR图像目标恒虚警检测算法,该算法首先对待测像素的参考窗进行基于ODVI的自适应筛选处理(Automatic Censoring, AC),以去除窗内的强杂波和干扰像素,并以窗内保留的均匀像素对背景的统计特性进行建模,估计其概率密度函数的参量,同时构建双参数恒虚警检测(CFAR)的检验统计量,计算检测的自适应阈值,实现检测的判决。论文给出了该算法的检测性能曲线,并利用实测的X波段SAR图像进行实验验证,与其它检测方法进行比较,结果显示该文算法具有较好的检测性能和较低的虚警概率。     

Performance analysis of the modified versions of CFAR detectors in multiple-target and nonuniform clutter

A constant false alarm rate (CFAR) in the presence of variable levels of noise is usually a requirement placed on any modern radar. The CA- and OS-CFAR detectors are the most widely used ones in the CFAR world. The cell-averaging (CA) is the optimum CFAR detector in terms of detection probability in homogeneous background when the reference cells have identical, independent and exponentially distributed signals. The ordered-statistic (OS) is an alternative to the CA processor, which trades a small loss in detection performance, relative to the CA scheme, in ideal conditions for much less performance degradation in nonideal background environments. To benefice the merits of these well-known schemes, two modified versions (MX- and MN-CFAR) have been recently suggested. This paper is devoted to the detection performance evaluation of these modified versions as well as a novel one (ML-CFAR). Exact formulas for their false alarm and detection performances are derived, in the absence as well as in the presence of spurious targets. The results of these performances obtained for Rayleigh clutter and Rayleigh target indicate that the MN-CFAR scheme performs nearly as good as OS detector in the presence of outlying targets and all the developed versions perform much better than that processor when the background environment is homogeneous. When compared to CA-CFAR, the modified schemes perform better in ideal conditions, and behave much better in the presence of interfering targets.     

Automatic censoring CFAR detector for heterogeneous environments

In radar detection, many constant false alarm rate (CFAR) processors have been proposed in the literature. It is well known that a processor is optimal only for one type of environment and that its detection performances are seriously degraded in presence of unknown irregularities. In such situations, the main difficulty resides in the estimation of the background configuration. That is, depending upon the non-homogeneity of the environment, one would choose the adequate optimal detection algorithm among a variety of known conventional ones that offer the best detection probability. Based on unknown transitions; i.e., in the presence of a priori unknown numbers of interfering targets and/or clutter edge, we propose an automatic censoring CFAR (AC-CFAR) detector for heterogeneous Gaussian clutter. The censoring technique used in this work offers a good discrimination between homogeneous and non-homogeneous environments. The proposed detector dynamically switches to the optimal conventional detector CA-, CMLD- or TM-CFAR. The performances of the proposed detector is evaluated and compared to existing detectors in various background situations. Monte Carlo simulations show that the AC-CFAR detector performs like the CA-CFAR in a homogeneous background. Moreover, the proposed detector exhibits considerable robustness in the presence of interfering targets and/or clutter-edge situations.     

Penalized Gaussian mixture probability hypothesis density filter for multiple target tracking

Bayesian multi-target filter develops a theoretical framework for estimating the full multi-target posterior which is intractable in practice. The probability hypothesis density (PHD) is a practical solution for Bayesian multi-target filter which propagates the first order moment of the multi-target posterior instead of the full version. Recently, the Gaussian Mixture PHD (GM-PHD) has been proposed as an implementation of the PHD filter which provides a close form solution. The performance of this filter degrades when targets are moving near each other such as crossing targets. In this paper, we propose a novel approach called penalized GM-PHD (PGM-PHD) filter to improve this drawback. The simulation results provided for various probabilities of detection, clutter rates, targets velocities and frame rates indicate that the proposed method achieves better performance compared to the GM-PHD filter.     

Exact analysis of OS modified versions with noncoherent integration

This paper deals with the exact detection analysis of the Ordered-Statistic(OS) processor along with OS Greatest Of(OSGO) and OS Smallest Of(OSSO) modified versions, for Mpostdetection integrated pulses when the operating environment is nonhomogeneous. Analytical results are presented in multiple-target case as well as in regions of clutter power‘transitions. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the SWII target fluctuation model. As the number of noncoherently integrated pulses increases,lower threshold values and consequently better detection performances are obtained in both homogeneous and multiple target background models. However, the false alarm rate performance of OSSO-CFAR(Constant False Alarm Rate) scheme at clutter edges is worsen with increasing the postdetection integrated pulses. As predicted, the OSGO-CFAR detector accommodates the presence of spurious targets in the reference window, given that their number is within its allowable range in each local window, and controls the rate of false alarm when the contents of the reference cells have clutter boundaries.     

杂波图CFAR平面技术在均匀背景中的性能

典型的杂波图恒虚警检测技术常被用于时域平稳、空域变化较剧烈的杂波环境。它在每个杂波图单元处都形成一个独立的检测阈值,于是被称为杂波图ＣＦＡＲ点技术。而沈福民提出了一种杂波图ＣＦＡＲ平面技术的构想。这是一种在时空两维变化都比较平稳的杂波环境情况下尤为适用的杂波图ＣＦＡＲ检测技术,我们将其称为面技术。本文具体推导了这种技术在时空两维都均匀的背景中的检测性能表达式,并同杂波图点技术的性能进行较细致的分析