一种改进的VI-CFAR检测器

修正的削减平均MTM（modified trimmed mean）恒虚警（constant false alarm rate, CFAR）算法通过对前后滑窗的削减平均再求和实现杂波功率估计,其在多目标环境下具有很好的抗干扰性能。为了提高VI检测器在多目标背景尤其是前后滑窗都存在干扰目标时的检测性能,将MTM算法应用于VI（variability index）检测器,提出了一种改进的恒虚警检测器（VIMTM）,该检测器的检测阈值由CA、GO和MTM算法产生。同时本文推导了MTM算法标称化因子TMTM的表达式,在SwerllingⅡ假设下,对VIMTM在不同的杂波背景下的性能进行了仿真分析,并与VI和基于OS (order statistic )的OSVI进行了比较。结果表明,在均匀环境和多目标背景下,VIMTM检测性能较好,且具有更强的鲁棒性;在杂波边缘背景下,VIMTM控制虚警的能力与VI、OSVI相当。另外,与OSVI相比,VIMTM缩短了参考样本的排序时间,提高了检测器的工作效率。      

OSSO和OSGO恒虚警检测器在Pareto分布混响背景下的性能分析

Pareto分布是一种重要的非高斯分布,被证明能够有效描述高分辨率主动声纳的混响统计特性。文章分析了有序统计选小(Ordered Statistic with Smallest Option, OSSO)和有序统计选大(Ordered Statistic with Greatest Option, OSGO)两种恒虚警(Constant Fales Alarm Rate, CFAR)检测器在Pareto分布混响背景下的性能。在尺度参数已知情况下,证明了OSSO-CFAR和OSGO-CFAR对形状参数具有恒虚警的特性。进一步分析了两种检测器在均匀Pareto混响背景、多目标干扰及混响边缘情况下的性能,并与有序统计(Ordered Statistic, OS)CFAR进行了对比。结果表明,在均匀混响背景下,OSGO-CFAR的检测性能与OS-CFAR相近,在混响边缘情况下具有最好的虚警控制能力;而对于多目标干扰情况,OSSO-CFAR比其他两种检测器的检测性能更优。      

OSCA-CFAR在K分布杂波背景下的性能分析

本文证明了形状因子已知条件下有序统计平均（OSCA）恒虚警检测器在K分布杂波背景下具有恒虚警性能，分析了均匀K分布杂波背景和多目标情况下该检测器的性能，并与OS和OSGO-CFAR进行了比较，仿真结果表明OSCA在两种环境下均具有最好的检测性能。     

一类基于VMSR的恒虚警检测器

设计了一类基于VMSR的改进型恒虚警检测器,给出了检测器的检测结构框图,同时描述了该类检测器的检测模型并给出了相应的检测概率表达式。该类检测器通过VR删除算法自适应删除杂波背景中的干扰样本,并将剩余样本按照不同操作得到杂波背景功率估计,提高了复杂杂波背景下检测器的精确性及鲁棒性。通过与典型VMSR-CFAR检测器的仿真对比实验,验证了该类检测器的检测性能。     

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。     

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。     

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.     

基于UMVE算法的恒虚警检测器

基于无偏最小方差估计（UMVE）算法，提出了一种新的恒虚警检测器（UMVEM-CFAR）。它的前沿和后沿滑窗均采用UMVE算法来产生局部估计，再对两者求和得到背景功率水平估计。在SwerlingⅡ型目标假设下，推导出UMVEM-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只的解析表达式，与OS—CFAR相比，UMVEM在均匀背景和多目标环境下均具有最好的检测性能，并且它的处理时间只有OS的一半。     

一种基于检测统计量的SOSGO-CFAR 检测算法

提出了一种新的恒虚警检测算法SOSGO-CFAR.该算法应用检测单元采样作为选择参考单元的依据,使用了基于转换恒虚警(S-CFAR)和排序选大恒虚警(OSGO-CFAR)的复合算法.文章给出了该算法在均匀背景中的数学分析.并在均匀背景、杂波边缘和多目标情况下,用MonteCarlo方法进行了仿真分析.结果表明,该检测器既具有均匀背景下和CA-CFAR相近的良好性能,在杂波边缘环境中,具有接近OSGO-CFAR的性能,且在多目标环境中,其性能明显好于S-CFAR.     

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.     

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.     

A robust auto‐adaptive approach for serial acquisition of generalized multi‐carrier spread spectrum in multipath fading mobile channels

In this paper, a new robust auto‐adaptive approach for pseudo‐noise (PN) code acquisition is proposed. It is applied to the generalized multi‐carrier direct‐sequence code‐division multiple‐access (MC DS‐CDMA) systems communicating over frequency‐selective multipath Rayleigh fading channels. This new approach is based on the constant false alarm rate (CFAR) detection algorithm, referred here as automatic selection partial sum ordered statistics (ASPSOS)‐CFAR. The proposed approach does not require any prior information about the background environment and uses maximum likelihood estimation (MLE) method to detect the interfering signals group in the ranked cells for the full reference window. Once this group is identified and censored, the remaining smaller ranked cells are combined to form an estimate of the background noise level to compute the adaptive threshold. Through simulations, the performance of the proposed detector is analyzed and compared with traditional CFAR detectors based on fixed or automatic censoring algorithms. The obtained results show that the proposed detector eliminates the drawbacks of the previously related detectors and offers a robust detection performance to enhance the acquisition process in heterogeneous background environments.     

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。     

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.     

Heterogeneous performance evaluation of sophisticated versions of CFAR detection schemes

The detection of radar targets in a background, the statistical parameters of which are unknown and may not be stationary, can be effectively achieved through CFAR processors. The CA-CFAR scheme performs optimally for homogeneous and exponentially distributed clutter observations. However, it exhibits severe performance degradation in the presence of outlying target returns in the reference set or in regions of abrupt change in the background clutter power. The OS-CFAR processor has been proposed to solve both of these problems. Although this processor may treat target multiplicity quite well, it lacks effectiveness in preventing excessive false alarms during clutter power transitions. The TM-CFAR algorithm, which implements trimmed averaging after ordering, can be considered as a modified version of OS technique. By knowingly trimming the ordered samples, the TM detector may actually perform better than the OS processor. To simultaneously exploit the merits of CA, OS, and TM schemes, two combinations namely CAOS and CATM have been suggested. Each one of these versions optimizes good features of two CFAR detectors, depending on the characteristics of clutter and searched targets, with the goal of enhancing the detection performance under constant level of false alarm. It is realized by parallel operation of two standard types of CFARschemes. Our goal in this paper is to analyze these two developed versions in heterogeneous situations, to show to what extent they can improve the behavior of the conventional CFAR processors.