S-CFAR的改进型SOS-CFAR

针对S-CFAR在杂波边缘虚警控制能力较差的问题,文中基于S-CFAR的检测单元统计特性和OS-CFAR的有序统计特性提出了SOS-CFAR,并按照一定的参数选择顺序对SOS-CFAR参数进行了选择,最后仿真对比了几种CFAR的检测性能及虚警控制能力。仿真分析表明,SOS-CFAR在均匀环境和多目标强干扰环境下的检测性能与S-CFAR相当,且在杂波边缘的虚警控制能力有较大的改善。     

一种非均匀Weibull杂波背景中的CFAR检测器

针对传统恒虚警(CFAR)算法在非均匀环境下,待检测单元(CUT)与参考窗的分辨单元不具有独立同分布(IID)特性,检测器性能出现剧烈下降的问题,提出一种新的CFAR检测器。该检测器首先引入一种M-N杂波边缘二元积累实现非均匀杂波边缘提取;然后,对数据平面内相邻杂波边缘内的数据,利用一种地形特征分类算法实现对地形的分类编号;最后,根据地形编号选择与CUT相同地形的分辨单元作为参考单元实现CFAR检测,则所选择的参考单元与CUT具有IID特性。利用实测数据验证M-N杂波边缘二元积累检测算法和地形特征分类算法的有效性。计算机仿真证明:文中提出的CFAR检测器的性能,比传统CFAR检测器的性能有明显提升。     

An improved reduced-rank CFAR space-time adaptive radar detectionalgorithm

The CFAR test developed in previous work is a normalized ratio test for signals in nonwhite Gaussian noise. However, in the airborne radar environment, the noise consists of strong interference and a relatively weak thermal noise, in the case of a large interference-to-thermal noise ratio, this test can be simplified to the reduced-rank CFAR test developed previously, which operates in an interference-free subspace without the need for matrix inversion operations. This test is extended in this paper to one that includes both the primary and secondary data as defined by Bose and Steinhardt (see ibid., vol.43, p.2164-75, 1995), it is also shown that this test can be modified to obtain a dramatically improved performance. A much smaller amount of sample data is needed in this new improved algorithm to achieve a given probability of detection than is required by this test. Finally, the performance of this new reduced-rank CFAR test statistic is analyzed, and a simulation is performed for an example scenario in order to validate the theoretical results     

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。     

UWB SAR非均匀区域目标检测方法

在超宽带合成孔径雷达(UWB SAR)图像目标检测中不仅会遇到均匀杂波区域，还会遇到叶簇区域、空旷区域及干扰目标等组成的非均匀杂波区域。恒虚警率(CFAR)目标检测是雷达目标检测的重要方法，而传统的CFAR检测对UWB SAR非均匀杂波区域目标检测效果较差。首先分析了叶簇区域、空旷区域及二者混合区域的杂波分布。然后针对UWB SAR的实际情况，运用智能索引变量的CFAR检测技术(VI—CFAR)，使得均匀杂波和非均匀杂波背景中目标检测都取得了较好的效果。最后，利用实际UWB SAR目标检测结果验证了VI—CFAR的有效性。     

一种基于检测单元统计特性的CFAR

针对在多目标、强干扰非均匀环境下CA-CFAR、OS-CFAR检测器的检测性能严重下降的问题,本文提出一种基于检测单元统计特性来选择参考单元的CFAR.分析结果表明,相比于CA-CFAR、OS-CFAR,该CFAR在均匀环境中具有较小的损失,在非均匀环境中,具有较强的抗干扰能力.     

The CFAR adaptive subspace detector is a scale-invariant GLRT

The constant false alarm rate (CFAR) matched subspace detector (CFAR MSD) is the uniformly most-powerful-invariant test and the generalized likelihood ratio test (GLRT) for detecting a target signal in noise whose covariance structure is known but whose level is unknown. Previously, the CFAR adaptive subspace detector (CFAR ASD), or adaptive coherence estimator (ACE), was proposed for detecting a target signal in noise whose covariance structure and level are both unknown and whose covariance structure is estimated with a sample covariance matrix based on training data. We show here that the CFAR ASD is GLRT when the test measurement is not constrained to have the same noise level as the training data, As a consequence, this GLRT is invariant to a more general scaling condition on the test and training data than the well-known GLRT of Kelly (1986)     

基于参量检测的CFAR处理分析与实现

雷达信号的恒虚警率（CFAR）处理是现代雷达信号处理的重要内容。文中介绍了在杂波、噪声中检测目标的基本原理，并用参量CFAR检测方法分析了以瑞利杂波条件为例的恒虚警性能，最后给出了某新型火控雷达恒虚警电路的具体实现方法。     

基于雷达知识库的知识辅助恒虚警检测算法

在非均匀杂波背景下,传统的恒虚警检测算法,比如CA鄄CFAR,所选择的参考单元与待检测单元往往无法满足独立同分布的条件,导致背景杂波功率水平的估计值存在偏差,使得检测性能降低。针对上述问题,文中提出了一种基于雷达知识库的知识辅助恒虚警检测算法。首先,利用雷达环境知识来构建动态的雷达知识库;然后,利用雷达知识库中的先验信息来辅助参考单元的选择,提高背景杂波功率水平的估计准确性,从而降低非均匀背景带来的影响;最后,利用线性调频连续波雷达采集的实测数据对该算法性能进行了验证。结果表明:在非均匀杂波环境下,基于雷达知识库的知识辅助恒虚警检测算法比传统算法有更好的检测性能。     

基于子带矩阵CFAR的海面慢速小目标检测算法

对于K分布海杂波环境下的目标检测,基于信息几何理论的矩阵CFAR检测器是一种有效的目标检测方法。但矩阵CFAR方法计算复杂度高且当目标多普勒频率严重偏离杂波频谱中心时,其检测性能不如自适应归一化匹配滤波器(ANMF)方法,影响其实际应用。为此,该文以滤波器组对接收信号进行滤波处理,提出一种基于滤波器组子带分解最大特征值的矩阵CFAR检测方法(FD-MEMD),通过双杂波抑制来解决目标多普勒频率偏离杂波频谱中心时矩阵CFAR方法失效的难题。最后,仿真实验验证了所提FD-MEMD具有较好的检测性能。     

One- and Two-Stage Tunable Receivers

In this paper, we propose and assess a CFAR detector that can adjust its “directivity” through a real scalar parameter. It relies on the usual assumption that a set of homogeneous training data is available and encompasses as special cases the well-known Kelly's GLRT and the recently introduced W-ABORT detector. More important, it can be tuned in order to control the level to which sidelobe signals are rejected. Such functionality is particularly important to contain the number of false alarms in presence of mismatched signals. We also consider a parametric detector which resorts to a diagonally loaded sample covariance matrix commonly adopted to take advantage of the presence of strong interferers. The performance assessment of such detector has shown that it can significantly outperform Kelly's GLRT in terms of prediction probabilities for matched signals and in terms of selectivity, but it is not strictly CFAR. We also propose to use the CFAR parametric detector as second stage of a two-stage tunable detector and show that such a two-stage detector can outperform already proposed tunable receivers in terms of selectivity. The analysis of the detectors is conducted assuming a homogeneous Gaussian environment; with reference to this scenario and to the CFAR detectors we derive analytical expressions for the probability of false alarm and the probability of detection for both matched and mismatched signals.      

基于GIS信息的CFAR检测器

在雷达信号检测过程中,为了实现恒虚警处理,必须采用动态门限。恒虚警检测器的门限设置通常是利用待检测单元附近的距离单元杂波数据进行计算得到的。然而,杂波环境的非均匀性导致了杂波功率随着距离变化剧烈,常规的恒虚警检测器性能会显著下降。文中给出了基于地理信息系统的恒虚警检测算法,利用对杂波环境的了解程度,可以显著提高CFAR检测器的性能。利用IPIX雷达实测数据,验证了该算法性能优于常规的其他CFAR处理器。     

低误报率的ADS-B电文报头检测方法

针对ADS-B电文报头检测中的误报问题,提出双门限报头检测方法.该方法根据本地报头与接收信号滑动相关的特点,分别针对关注信号与背景信号给出了恒虚警(CFAR)门限和半幅度门限,综合双门限进行两次检测判决可使方法适用于低信噪比环境,并在进行循环冗余校验之前显著地抑制误报.仿真结果表明,双门限检测方法在低信噪比区间具备与C...     

A CFAR adaptive subspace detector for second-order Gaussian signals

We study the problem of detecting subspace signals described by the Second-Order Gaussian (SOG) model in the presence of noise whose covariance structure and level are both unknown. Such a detection problem is often called Gauss-Gauss problem in that both the signal and the noise are assumed to have Gaussian distributions. We propose adaptive detectors for the SOG model signals based on a single observation and multiple observations. With a single observation, the detector can be derived in a manner similar to that of the generalized likelihood ratio test (GLRT), but the unknown covariance structure is replaced by sample covariance matrix based on training data. The proposed detectors are constant false alarm rate (CFAR) detectors. As a comparison, we also derive adaptive detectors for the First-Order Gaussian (FOG) model based on multiple observations under the same noise condition as for the SOG model. With a single observation, the seemingly ad hoc CFAR detector for the SOG model is a true GLRT in that it has the same form as the GLRT CFAR detector for the FOG model. We give an approximate closed form of the probability of detection and false alarm in this case. Furthermore, we study the proposed CFAR detectors and compute the performance curves.     

七种恒虚警率处理方案及性能分析

通过分析现有的五种恒虚警率 (CFAR)处理器检测性能的优劣 ,提出了两种改进型CFAR方案 ,并在计算机模拟统计的基础上对七种处理器的检测性能进行了分析比较 ,得出准选大恒虚警率 (PGO CFAR)和准有序恒虚警率 (POS CFAR)是两种较为实用的在复杂杂波环境中工作的恒虚警处理方案 ,且各有优越性。     

K分布海杂波背景下基于最大特征值的雷达信号检测算法

针对K分布海杂波背景下的恒虚警检测问题,基于信息几何的矩阵CFAR检测器具有较好的检测性能,但其计算复杂度较高,从而影响其实际应用。该文根据奈曼-皮尔逊准则,推导了似然比检测统计量与最大特征值之间的关系,进而提出了基于最大特征值的矩阵CFAR检测方法(M-MED)。最后通过对所提方法的计算复杂度及仿真实验结果的分析表明了所提方法不仅计算复杂度低且具有较好的检测性能。     

一种新的恒虚警处理方法简介

对一种二阶段CFAR处理器作了简单介绍,这种处理器的第一阶段利用经验贝叶斯推断方法和杂波区域的空间连续性确定均匀杂波区域,第二阶段是对均匀杂波区域,用标准的CFAR处理方法,也就是通常要求参考单元独立同分布的CFAR处理方法完成恒虚警处理。这样的方法适用于非均匀的环境,如分布不均匀的杂波边缘,而且即使改变外界环境也能给出相对稳定的结果。     

恒虚警慢门限量化器的理论与应用设计

根据恒虚警慢门限的理论,讨论了量化门限间接随噪声电平变化保持恒虚警的必要性及恒虚警慢门限噪声采样区的自动有效判别问题,提出了一种新在和复杂环境下采样区的自动有效判别及门限自动调整方案。     

基于窗口参数估计的距离扩展目标CFAR检测

雷达在探测目标时,若目标尺寸远大于雷达的距离分辨率,会在距离维上呈现出距离扩展目标特性,采用传统的点目标检测方法因无法利用全部能量而造成检测性能下降。本文针对距离扩展目标检测问题,以外辐射源雷达为背景,首先建立了外辐射源雷达距离扩展目标回波模型,提出了基于最大广义信噪比准则的距离扩展窗口参数估计方法来预估距离扩展目标的位置与宽度;为进一步判断窗口内是否为目标,研究了距离扩展窗口内的模糊恒虚警率（CFAR）检测算法,仿真比较了基于不同积累准则对各类起伏目标的模糊CFAR检测性能,并与传统的二进制积累CFAR比较,表明在均匀噪声环境和多目标环境下,基于模糊代数积积累的模糊CFAR检测性能均最优。最后结合外辐射源雷达舰船目标探测实验的实测数据处理结果,证明了基于模糊代数积积累的模糊CFAR具有最优的检测性能。     

利用样本协方差矩阵特征值分解实现双通道SAR动目标检测

该文针对机载双通道SAR-GMTI系统及实测数据,提出一种新的地面慢动目标检测方法。该方法基于双通道样本协方差矩阵特征值分解,将杂波第2特征值和干涉相位联合统计特性的研究结果用于慢动目标检测,即根据给定的恒虚警概率确定一条联合分布的第2特征值干涉相位等高线作为门限检测曲线,同时结合第2特征值、干涉相位门限预处理,实现地面慢动目标的精确检测。实测数据实验结果表明:该方法不但扩大了慢动目标的可检测速度范围,同时还降低了系统的虚警概率。