一种新的最大选择恒虚警检测器

本文提出了一种基于有序统计和单元平均产生局部估计，并应用最大选择产生检测单元杂波功率水平估计Ｚ的新的恒虚警检测器。我们推导出了该检测器在ＳｗｅｒｌｉｎｇⅡ型目标假设下的虚警概率、检测概率和度量平均判决门限解析表达式。分析了它在均匀背景和强干扰环境中的检测性能。     

一种新的基于剔除平均的最大选择恒虚警检测器

本文基于剔除平均(TM)提出了一种新的最大选择(GO)恒虚警检测器,它的前、后沿滑窗均采用TM来产生局部估计,再选择两者之中的最大值作为检测器对杂波功率水平的估计,去设置自适应检测门限,并应用了何友(1994)提出的自动筛选技术。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,并且它的样本排序时间还不到OS的一半。一些流行的恒虚警方法如GO、GOSGO或OSGO、CMGO可看作是TMGO的特例。     

基于有序统计和自动删除平均的最大选择恒虚警检测器

基于有序统计(OS)方法和自动删除单元平均(ACCA)方法提出一种新的恒虚警检测器(OSACGO)以提高CFAR检测的性能,它采用OS和ACCA产生两个局部估计,然后取二者中最大值作为背景功率水平估计,从而设置自适应检测门限.在SwerlingⅡ型目标假设下,推导出OSACGO在均匀背景下虚警概率Pfa的解析表达式.通过与其它现有方案进行比较,结果表明在均匀背景及多目标和杂波边缘引起的非均匀背景中,OSACGO均具有相当好的检测性能,而它的样本排序时间只有OS和ACCA的一半.     

秩和非参数检测器在杂波边缘中的性能

人们常用Rohling教授提出的3种典型背景即均匀背景、多目标和杂波边缘来对检测器的恒虚警率(CFAR)性能进行衡量,但在现有的文献中缺乏秩和(RS)非参数检测器在杂波边缘中虚警概率的解析表达式,缺乏RS检测器与经典的参量型恒虚警率(CFAR)检测器在杂波边缘中虚警控制能力的比较,这在理论研究上是不完整、不全面的。该文给出了RS检测器在杂波边缘中虚警概率的解析表达式,并比较了它与非相干积累单元平均(CA),选大(GO)和有序统计(OS)恒虚警方法在杂波边缘中的虚警控制能力。可以看出,在强、弱杂波均为瑞利分布的情况下,RS检测器在杂波边缘的虚警控制能力处于非相干积累CA方法和非相干积累OS方法之间。但是当长拖尾分布的非高斯杂波进入参考滑窗时,非相干积累CA, GO和OS参量型检测方法的虚警概率都产生了3个以上数量级的上升,且不能回到原始设定的虚警概率。而RS检测器显示出了非参量检测器的优势,即当杂波背景的分布类型发生变化后,它仍然可以保持虚警概率的恒定。     

韦布尔杂波下非参数量化秩检测器的性能

 非参数量化秩(RQ)恒虚警率检测器在雷达目标检测中占据着重要的地位,本文采用解析的方法分析了量化秩检测器在韦布尔分布中的检测性能,并考虑了均匀杂波背景和多目标环境情形,目标模型为Swerling II型.本文给出了非参数量化秩检测器在韦布尔杂波背景中的虚警概率和检测概率的解析计算表达式,并与参量型单元平均(CA)检测器的检测性能进行了对比.     

基于删除平均和单元平均的CFAR检测器性能分析

基于删除平均(CM)和单元平均(CA),提出了一类恒虚警率(CFAR)检测器,其采用CM和CA产生局部估计,再对局部估计进行平均、选大、选小等逻辑运算实现对杂波功率的估计。分析了所提3种检测器的在均匀和非均匀背景下的检测性能。结果表明,在均匀背景和多目标环境下,对局部估计进行平均的检测器性能最优。     

基于自动删除算法的最大选择恒虚警检测器

基于自动删除单元平均(ACCA)恒虚警算法,提出一种新的恒虚警检测器(ACCAGO-CFAR)以提高CFAR检测的性能。它的前沿和后沿滑窗均采用ACCA算法来产生2个局部估计,取其中最大值作为总的背景功率水平估计,从而设置自适应检测门限。在SwerlingⅡ型目标假设下,推导出ACCAGO-CFAR在均匀背景下虚警概率P_(fa)的解析表达式。通过与其他现有方案进行比较,结果表明ACCAGO在均匀背景及多目标和杂波边缘引起的非均匀背景中,均具有较好的检测性能,尤其是在杂波边缘环境中,它的虚警尖峰比ACCA小近2个数量级,并且处理时间也比ACCA大为缩短。     

一种新的基于UMVE和OS的恒虚警检测算法

为了充分利用参考单元所提供的信息,减少恒虚警损失,该文基于无偏最小方差估计(UMVE)方法和有序统计(OS)方法,提出了一种新的恒虚警检测器(MOSUM-CFAR)。它的前沿和后沿滑窗分别采用UMVE和OS方法产生两个局部估计,再对二者求和得到背景功率水平估计。在Swerling II型目标假设下,文中推导出MOSUM-CFAR在均匀背景下虚警概率Pfa和检测概率Pd及多目标环境下检测概率Pd的解析表达式,并与其它方案作了比较。分析结果表明MOSUM-CFAR在均匀背景和多目标环境下均具有相当好的检测性能。     

量化秩非参数CFAR检测器在杂波边缘中的性能分析

人们常用均匀背景、多目标和杂波边缘3种典型背景来衡量雷达目标检测器的性能,但在现有文献中缺乏量化秩（Rank Quantization,RQ）非参数检测器在杂波边缘中虚警概率的理论模型,缺乏RQ非参数检测器与经典的参量型检测器在杂波边缘中虚警控制能力的比较.本文给出了RQ检测器在杂波边缘中虚警概率的解析表达式,并比较了它与非相干积累CA （Cell Averaging）,GO （Greatest Of）,OS （Ordered Statistic）恒虚警方法在杂波边缘中的虚警控制能力.可以看出,采用高秩量化门限的RQ检测器的虚警控制能力要优于低秩量化门限的情况,在瑞利分布杂波边缘情况下,RQ检测器的虚警控制能力与非相干积累OS方法接近.但是当强杂波变为长拖尾分布的非高斯杂波时,非相干积累CA,GO和OS参量型检测方法的虚警概率产生了3个数量级以上的上升,且不能降回到原始设定的虚警概率.而RQ检测器显示出了非参量检测器的优势,即当杂波背景的分布类型发生变化后,它仍然可以保持虚警概率的恒定.     

一种新的基于单元逼近的恒虚警率检测器

基于删除平均（CM）和单元平均（CA）提出了一种新型的恒虚警率检测器,它采用CM和CA产生局部估计,再将这两个局部估计与检测单元进行比较,取逼近于检测单元的局部估计作为总的杂波功率估计。在SwerlingⅡ型目标假设和高斯杂波下,推导出它的检测概率Pd和虚警概率Pfa的解析表达式。     

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.     

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.     

方差均值平方比恒虚警检测器

为了提高雷达恒虚警(Constant False Alarm Rate,CFAR)检测器在多目标背景下的鲁棒性,更好地检测目标,提出了一种新的基于方差均值平方比(VMSR)的恒虚警检测器,并建立了相应的检测器模型,得出了标称化因子T值和置信区间(a,b)。在均匀背景和多目标背景下,对VMSR检测器进行了仿真分析。在均匀背景下,VMSR检测性能优于OS,相比CA仅有很小的检测损失;在多目标背景下,VMSR检测性能相比OS得到了提升,特别是在干扰目标个数r>N-k时,OS不能有效检测出目标,而VMSR仍能保持较好的检测性能。结果表明,VMSR在多目标背景下检测性能优于OS,其在多目标背景下具有较强的鲁棒性。     

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.     

OSCAGO－CFAR检测器在干扰边缘中的性能分析

本文研究ＯＳＣＡＧＯ－ＣＦＡＲ检测器［１，２］在干扰边缘中的性能。文中推导出了它在干扰边缘环境中虚警概率的解析表达式，分析了它抗边缘干扰的性能，并且与ＧＯ、ＯＳ和ＣＡ－ＣＦＡＲ检测器进行了比较。结果表明，ＯＳＣＡＧＯ的抗边缘干扰性能比这三种ＣＦＡＲ检测器均有明显增强。同时，它在均匀背景和多目标环境中也保持了良好的检测性能。     

CFAR Detectors for MIMO Radars

CFAR (Constant False-Alarm Rate) processors are useful for detecting radar targets in a background for which the parameters in the statistical distribution are not known. A variety of CFAR techniques such as CA (Cell Averaging), Go (Greatest Of), SO (Smallest Of), OS (Ordered Statistics) and ACMLD (Automatic Censored Mean-Level Detector) processors have been proposed for SISO (Single Input–Single Output) radars in a non-homogeneous background. In this paper, conventional CFAR algorithms including CA, SO, OS and ACMLD processors are generalized for MIMO (Multiple Input–Multiple Output) radars. The exact expressions for false-alarm probabilities of the proposed algorithms in a homogeneous background are presented. In addition, the detection performance of the proposed detectors is studied by means of simulation in the presence of interfering targets and also colored Gaussian clutter. Besides, the proposed CFAR processors are compared, and it is shown that the ACML-based algorithm is superior to the other investigated methods.     

单元筛选后作最小选择的CFAR自适应检测器

为提高雷达自适应检测能力，本文针对多个干扰目标背景提出一种新的CFAR自适应检测方法。在斯威林2型目标的假设下对这种检测方法进行了检测性能分析，理论推导出均匀杂波背景和强干扰目标存在的情况下虚警概率和检测概率的数学模型，并把它同CA、GO、SO(smallest of)等检测方法用MATLAB仿真软件进行了比较。结果表明，这种方法在均匀杂波背景和多个干扰目标背景情况下都有很好的检测性能，尤其是多目标干扰条件下具有更大的优势。     

Performance comparison of a linearly combined ordered-statistic detectors under postdetection integration and nonhomogeneous situations

Several Constant False Alarm Rate （CFAR） architectures, where radar systems often employ them to automatically adapt the detection threshold to the local background noise or clutter power in an attempt to maintain an approximately constant rate of false alarm, have been recently proposed to estimate the unknown noise power level. Since the Ordered-Statistics （OS） based algorithm has some advantages over the Cell-Averaging （CA） technique, we are concerned here with this type of CFAR detectors. The Linearly Combined Ordered-Statistic （LCOS） processor, which sets threshold by processing a weighted ordered range samples within finite moving window, may actually perform somewhat better than the conventional OS detector. Our objective in this paper is to analyze the LCOS processor along with the conventional OS scheme for the case where the radar receiver incorporates a postdetection integrator amongst its contents and where the operating environments contain a number of secondary interfering targets along with the primary target of concern and the two target types fluctuate in accordance with the Swerling Ⅱ fluctuation model and to compare their performances under various operating conditions.     

Analysis of the Generalized Order Statistics Constant False Alarm Rate Detector

In this paper, we present an architecture of the constant false alarm rate (CFAR) detector called the generalized order statistics (GOS) CFAR detector, which covers various order statistics (OS) and cell-averaging (CA) CFAR detectors as special cases. For the proposed GOS CFAR detector, we obtain unified formulas for the false alarm and detection probabilities. By properly choosing coefficients of the GOS CFAR detector, one can utilize any combination of ordered samples to estimate the background noise level. Thus, if we use a reference window of size N, we can realize (2^N - 1) kinds of CFAR processors and obtain their performances from the unified formulas. Some examples are the CA, the OS, the censored mean level, and the trimmed mean CFAR detectors. As an application of the GOS CFAR detector to multiple target detection, we propose an algorithm called the adaptive mean level detector, which censors adaptively the interfering target returns in a reference window.