基于CFAR的SAR目标检测

CFAR检测是由事先定好恒定虚警概率进行目标检测的一种算法,其前提是目标相对于背景具有较强的对比度。CFAR算法通过单个像素灰度和某一门限的比较达到检测目标像素的目的。文中研究了恒虚警概率检测算法,推导了不同拟合分布的具体形式,给出了几种代表性检测器,如CA-CFAR检测器,通过仿真结果证明,在均匀杂波区域中,3种检测器的结果相当,都能检测出目标。但从整体看,CA-CFAR的检测性能更好。     

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。     

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.     

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。     

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。      

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.     

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.     

基于压缩感知的CFAR目标检测在机会雷达中的应用

提出一种基于压缩感知(CS)技术在机会雷达系统中进行恒虚警率(CFAR)目标检测的算法,根据目标回波在距离单元上的稀疏性,采用压缩感知技术对目标回波进行压缩采样;设计了一种新的建立在压缩域上的CA-CFAR检测器,它能在不恢复原始信号的条件下,快速完成目标回波的检测;进行了检测门限理论分析,设计出一种适用于压缩域检测的门限选定方法;给出系统检测结果与接收机的性能曲线。仿真结果表明,本算法可以实现低信噪比下雷达信号的直接检测,无需信号重构,节省了运算量。     

海杂波背景下的双参数单元平均恒虚警检测器

介绍了海杂波的幅度分布模型,指出了对于同一门限系数"尖锐"海杂波背景下的虚警率要明显高于噪声背景下的虚警率,提出了双参数单元平均恒虚警(CA-CFAR)检测器的结构框图,仿真分析了其检测性能.最后分别利用双参数CA-CFAR检测器和常规CA-CFAR检测方法对实测数据进行处理和比较,指出在虚警率一定的前提下前者相对于后两者在空间上可以自适应地改变门限系数以有效地抑制海杂波引起的高虚警.     

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。     

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.     

A generalized switching CFAR processor based on test cell statistics

In this paper, a new constant false alarm rate (CFAR) thresholding algorithm which is a generalisation of the switching CFAR (S-CFAR) that takes into account the statistics of the sample in the test cell for reference sample selection is proposed. It employs a composite approach based on the switching CFAR and the order statistic CFAR (OS-CFAR). A mathematical analysis in a homogeneous environment is provided for this detector. The results obtained show that the detection performance of the generalised S-CFAR (GS-CFAR) is improved both in homogenous background and non-homogenous environment caused by interfering targets and clutter edge.     

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.     

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。     

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。     

基于地形融合分类的分区二维恒虚警检测器

在非均匀杂波背景下,由于恒虚警( CFAR)检测器与杂波背景幅度分布不匹配,导致检测器性能出现剧烈下降。针对此问题,提出了一种基于地形融合分类的分区二维CFAR检测器。首先提出一种基于拟合优度( GoF)的地形融合分类算法来对非均匀杂波背景下的地形进行分类编号,同时记录各地形的幅度分布及幅度分布参数;再根据地形编号及记录的幅度分布选择相匹配的CFAR处理窗实现分区二维CFAR;最后利用实测数据验证了该地形融合分类算法的有效性,并用半实测、实测数据对所提出CFAR检测器性能进行了仿真验证,结果表明相比传统二维CFAR检测器,所提出的CFAR检测器在非均匀背景下性能有明显提高。     

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。     

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.     

Threshold optimization of decentralized CFAR detection in weibull clutter using genetic algorithms

The use of genetic algorithms (GAs) tool for the solution of distributed constant false alarm rate (CFAR) detection for Weibull clutter statistics is considered. An approximate expression of the probability of detection (P _D) of the ordered statistics CFAR (OS-CFAR) detector in Weibull clutter is derived. Optimal threshold values of distributed maximum likelihood CFAR (ML-CFAR) detectors and distributed OS-CFAR detectors with a known shape parameter of the background statistics are obtained using GA tool. For the distributed ML-CFAR detection, we consider also the case when the shape parameter is unknown of the Weibull distribution. A performance assessment is carried out, and the results are compared and given as a function of the shape parameter and of system parameters.