A novel threshold optimization of ML-CFAR detector in Weibull clutter using fuzzy-neural networks

This paper provides a novel and effective approach based on an adaptive neuro-fuzzy inference system for the solution of constant false alarm rate (CFAR) detection for Weibull clutter statistics. The optimal detection thresholds of the maximum-likelihood CFAR (ML-CFAR) and the Censored ML-CFAR (CML-CFAR) detectors in Weibull clutter with unknown shape parameter are obtained using fuzzy-neural networks (FNN) technique. The theory of the FNN is presented and the genetic learning algorithm (GA) is applied for the training of the FNN threshold estimator. The proposed FNN-ML-CFAR and FNN-CML-CFAR detectors proved to be efficient particularly in the case of spiky clutter. Experimental results showed the effectiveness of an adaptive neuro-fuzzy threshold estimator under different system conditions and it is also shown that the optimal FNN-ML-CFAR and FNN-CML-CFAR detectors can achieve better performances than the conventional ML-CFAR and CML-CFAR algorithms.     

机载雷达多杂波分布类型的恒虚警检测方法

本文提出了一种检验杂波分布类型的有效方法,该方法首先通过概率密度变换方法对被检验的杂波序列进行变换,再应用简单的正态分布检验方法检验变换后的序列,以此来检验原杂波序列的分布类型.针对常用的瑞利、韦布尔、对数正态杂波类型,与χ²和KS拟合检验方法进行了仿真比较,结果表明该方法检验精度高,计算简单,并且通用性强,克服了经典检验方法受区间划分影响大,对参数估计精度要求高,计算复杂的缺点.在杂波检验的基础上,根据OS-CFAR和log-t CFAR检测方法设计了适应于多杂波分布类型的CFAR处理器,对特定杂波类型CFAR检测器与背景杂波类型失配的各种情况的检测性能进行了仿真和分析.     

Log-normal分布杂波背景下有序统计恒虚警检测器性能分析

该文证明了在形状参数已知的均匀对数-正态(Log-normal)杂波背景下有序统计恒虚警(OS-CFAR)检测器能保持恒虚警的特性,导出了该背景下OS-CFAR检测器对非起伏目标的检测性能表达式和平均判决阈值(ADT)表达式。最后用数值方法讨论了最佳序值的选取以及OS-CFAR检测器在取不同参考单元数时相对于理想CFAR的信杂比损失。     

有序统计恒虚警（OS－CFAR）检测器在韦布尔干扰背景中的性能

本文研究韦布尔干扰背景前的有序统计恒虚警算法，并在均匀干扰背景中分析它的性能，对于未知的形状参数Ｃ，本文基于来自参考滑窗随机变量的期望和中值提出一种估计方法，同时在均匀干扰背景中研究了这种估计方法的性能，结果表明，这种估计方案具有很小的附加恒虚警损失，当Ｎ＝１６，Ｃ＝２，ｋ＝１２时，它的恒虚警损失比两参数有序统计恒虚警检测器小．．     

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。     

Optimum second threshold for the CFAR binary integrator in Pearson-distributed clutter

In this paper, we propose to analyze the binary integration of the cell-averaging constant false-alarm rate (CA-CFAR) and order statistics constant false-alarm rate (OS-CFAR) detectors in the presence of non-Gaussian spiky clutter modeled as a Pearson distribution. We derive new closed form expressions for false alarm and detection probabilities for the CA-CFAR detector in the presence of Pearson-distributed clutter backgrounds. We first show that the use of binary integration improves the detection probabilities of the detectors considered. Secondly, the maximum of detection probability occurs for an optimum choice when the second threshold is set to be equal to M = (3/4) L. For this optimum M-out-of-L rule, the comparison analysis of the CA-CFAR and OS-CFAR binary integrators showed that the latter has better performance in homogeneous Pearson- distributed clutter.     

广义Pareto分布海杂波背景下非相干检测器恒虚警性能分析

该文在广义Pareto分布海杂波背景下研究了单元平均(CA)和有序统计量(OS)两种非相干检测器的恒虚警(CFAR)性质,推导了两种非相干检测器的虚警概率公式,发现了两种检测器对杂波的尺度参数是恒虚警的。然而,两种检测器对杂波的散斑协方差矩阵结构和杂波形状参数是非恒虚警的。为了实现全场景的恒虚警检测,预先通过白化方法将具有相关性的海杂波去相关,并通过查表方法使用了匹配杂波形状参数、累积脉冲数和参考单元数的检测门限。在这种情况下,实验结果表明两种非相干检测器能确保全场景恒虚警。     

极不均匀合成孔径雷达杂波建模及恒虚警检测

基于合成孔径雷达(SAR)城区极不均匀杂波实际数据和Cramer-Von Mises距离,本文运用Rayleigh分布、Weibull分布、K分布和G分布对杂波进行了分布拟合检验.结果表明Rayleigh分布和Weibull分布难以拟合极不均匀场景回波,K分布只能部分拟合,而G分布的拟合程度最好.进而,针对G分布下的目标恒虚警检测问题,本文给出了不同参考单元条件下达到给定检测性能时信噪比随形状参数的变化曲线,分析了恒虚警(CFAR)性能损失随分布参数变化的原因,得到了确定恒虚警门限的有效方法.     

复杂环境下分布式多雷达系统检测性能仿真分析

分布式多雷达检测系统能够提高检测性能和适应性。针对Rayleigh杂波、杂波边缘、干扰目标三种环境,仿真分析了分布式有序统计恒虚警率（OS-CFAR）、分布式单元平均恒虚警率（CA-CFAR）的检测性能,并分析了融合方式选择、局部处理算法和参数调整对系统检测性能的影响,可为工程应用提供参考。     

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.     

Performance evaluation of OSSO-CFAR with binary integration in Weibull background

The performance of the Ordered-Statistic Smallest Of (OSSO) Constant False Alarm Rate (CFAR) with binary integration in Weibull background with known shape parameter is analyzed, in the cases that the processor operates in homogeneous background and non-homogeneous situation caused by multiple targets and clutter edge. The analytical models of this scheme for the performance evaluation are given. It is shown that the OSSO-CFAR with binary integration can greatly improve the detection performance with respect to the single pulse processing case. As the clutter background becomes spiky, a high threshold S of binary integration (S/M) is required in order to obtain a good detection performance in homogeneous background. Moreover, the false alarm performance of the OSSO-CFAR with binary integration is more sensitive to the changes of shape parameter or power level of the clutter background.     

Subspace detection for range and Doppler distributed targets with Rao and Wald tests

For the detection problem caused by the point target broken up into the distributed target in the high-range resolution radar system, adaptive detection algorithms based on the Rao and the Wald tests are proposed. The distributed target, which is modeled as a subspace random signal, may be distributed both in range and also in Doppler frequency axes. The clutter is modeled as a spherically invariant random process (SIRP). The unknown parameters are estimated by maximum likelihood estimation only under hypothesis H₀ in Rao test, while only under hypothesis H₁ in Wald test. Interestingly the proposed non-adaptive detectors from different deriving progresses have the same final form, which perform an incoherent processing over all the cells under test and ensure CFAR property with respect to the unknown statistics of the clutter texture component. In the end, performances of the proposed adaptive detectors are assessed through Monte Carlo simulations and are shown to have better detection performance compared with existing similar detectors.     

复杂条件下分布式OS-CFAR检测系统性能分析

对分布式恒虚警（ＣＦＡＲ）检测系统,特别是分布式统计排序（ＯＳ）ＣＦＡＲ检测系统的研究,往往局限于较为理想的简单条件。在本文中,通过对一个典型的３站分布式ＯＳ－ＣＦＡＲ检测系统在复杂条件（检测条件变化而且非一致）下检测性能的仿真和分析,研究了分布式检测系统在接近实际工作环境下的性能特点以及不同融合方式对检测环境变化的适应能力。结果表明,分布式检测ＣＦＡＲ系统在复杂条件下的性能与融合方式密切相关,其中“ＯＲ”融合分布式系统在条件变化而且非一致的检测环境中具有良好的鲁棒性。     

相参雷达K分布海杂波背景下非相干积累恒虚警检测方法

相参雷达系统下的非相干积累检测方法,可以提高雷达的目标检测速度,达到实时处理的要求。然而,相参雷达系统下的非相干积累检测方法对参考单元数、脉冲积累数、杂波散斑协方差矩阵以及海杂波模型的形状参数均是非恒虚警(CFAR)的。该文基于块白化的海杂波预白化方法,提出预白化单元平均恒虚警(PWCA-CFAR)检测方法和预白化单元中值恒虚警(PWCM-CFAR)检测方法,并使用了匹配于参考单元数、脉冲积累数、形状参数的自适应门限,确保提出检测方法的恒虚警特性。实验结果表明,当存在异常单元时,PWCM-CFAR检测方法的检测性能优于PWCA-CFAR检测方法。     

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。     

Robust radar detection of CA, GO and SO CFAR in Pearson measurements based on a non linear compression procedure for clutter reduction

This paper deals with the constant false alarm rate (CFAR) radar detection of targets embedded in Pearson distributed clutter. We develop new CFAR detection algorithms-notably cell averaging (CA), greatest of selection (GO) and smallest of selection SO-CFAR operating in Pearson measurements based on a non-linear compression method for spiky clutter reduction. The technique is similar to that used in non uniform quantization where a different law is used. It consists of compressing the output square law detector noisy signal with respect to a non-linear law in order to reduce the effect of impulsive noise level. Thus, it can be used as a pre-processing step to improve the performance of automatic target detection especially in lower generalised signal-to-noise ratio (GSNR). The performance characteristics of the proposed CFAR detectors are presented for different values of the compression parameter. We demonstrate, via simulation results, that the pre-processed compression procedure is computationally efficient and can significantly enhance detection performance.     

Gamma分布在海面目标检测中的应用

本文通过对大量实测数据的处理与定量分析,证明Gamma分布可用于X波段雷达海杂波幅度分布的建模,并介绍了两种适用于Gamma分布杂波背景的恒虚警（CFAR）检测器：基于最大似然估计的MLG-CFAR和基于加权幅度循环迭代的WAI-CFAR。理论分析证明,MLG-CFAR检测器对Gamma分布的杂波平均功率估计值是渐进有效的,且在形状参数已知的前提下,该检测器对尺度参数具有恒虚警特性,在均匀、非均匀杂波背景中的检测性能仿真结果验证了该检测器在Gamma杂波中的最优性;WAI-CFAR检测器综合了均值类和有序统计量类CFAR算法的优点,在均匀杂波背景中具有较好的性能,在非均匀杂波背景中可通过灵活设定加权系数,获得较强的目标检测能力和鲁棒的虚警控制能力。     

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.     

基于局部检测统计量的分布式CFAR检测理论研究

本文系统研究了融合中心接收局部检测统计量的分布式CFAR检测技术。在假设局部检测器背景杂波功率水平相同以及不同传感器测试单元之间服从独立同分布的前提下,首先推导了局部采用单元平均（CA）检测器时融合中心的检测概率与虚警概率的解析表达式,在此基础上利用Laplace变换的频域微分性质,将其推广到局部采用其它检测器,获得了普适性的融合方法,可适用于局部采用任意数量、形式的均值（ML）类和有序统计量（OS）类CFAR检测器时的情形,避免了传统的融合中心采用MOS类方法时所需要的局部信杂比相同的假设,实验结果表明了算法的有效性。      

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。