《雷达目标检测与恒虚警处理（第3版）》[68]百度网盘|亲测有效|pdf下载

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雷达目标检测与恒虚警处理（第3版）
出版社:清华大学出版社
作者:何友、关键、黄勇、简涛
出版时间:2023-02-01
热度:2270
上架时间:2025-03-08 06:13:50
价格:0.0

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本书是关于雷达目标检测和恒虚警(CFAR)处理理论及方法的一部专著。书中总结了三十多年来，在这一领域国际上的研究进展及大量研究成果。全书由15章组成，主要内容有经典的固定门限检测、均值类CFAR检测器、有序统计类CFAR检测器、采用自动筛选技术的广义有序统计类CFAR检测器、自适应CFAR检测器、韦布尔和对数正态杂波背景中的CFAR检测器、复合高斯分布杂波中的CFAR处理、非参量CFAR处理、杂波图CFAR处理、变换域CFAR处理、距离扩展目标检测、多传感器分布式CFAR处理及其他CFAR处理方法，后是本书的回顾、建议与展望。本书可供从事雷达工程、声纳、电子工程、信号与信息处理等专业的科技人员阅读和参考，还可以作为上述专业的研究生教材。

内容简介

本书是关于雷达目标检测和恒虚警(CFAR)处理理论与方法的一部专著。书中总结了三十多年来，在这一领域国际上的研究进展及大量研究成果。全书由15章组成，主要内容有经典的固定门限检测、均值类CFAR检测器、有序统计类CFAR检测器、采用自动筛选技术的广义有序统计类CFAR检测器、自适应CFAR检测器、韦布尔和对数正态杂波背景中的CFAR检测器、复合高斯分布杂波中的CFAR处理、非参量CFAR处理、杂波图CFAR处理、变换域CFAR处理、距离扩展目标检测、多传感器分布式CFAR处理以及其他CFAR处理方法，后是本书的回顾、建议与展望。本书可供从事雷达工程、声纳、电子工程、信号与信息处理等专业的科技人员阅读和参考，还可以作为上述专业的研究生教材

作者简介

何友教授，博士生导师，中国工程院院士，中共“十七大”代表，第十二届全国政协委员，中国人工智能和指挥与控制学会副理事长、中国航空学会名誉副理事长，全国优秀教师。获国家科学技术进步奖二等奖4项，获国家教学成果一、二等奖各1项，获省部级一等奖11项，授权中国发明专利和软件著作权60余项。获全国百篇优秀博士学位论文，在IEEE会刊等发表重要论文260余篇，出版专著6部。先后入选国家百千万人才工程，荣获何梁何利基金科学与技术进步奖、中国科协“求是”工程奖、全国留学回国人员成就奖、山东省科学技术奖等。研究领域包括信号检测、信息融合、智能技术与应用等。
关键教授，博士生导师。发表学术论文被三大检索收录100余篇，出版专著3部，授权国家发明专利30余项；获国家科学技术进步奖二等奖1项，省部级科学技术奖一等奖4项；获中国科协“求是”工程奖、全国优秀博士学位论文奖；“国家百千万人才工程”人选，省部级科技领军人才，山东省“泰山学者”特聘教授。研究领域包括雷达目标检测跟踪与识别、目标特性、海上目标探测等。

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目　　录

第1章绪论
Chapter 1Preface
Chapter 5The Generalized OrderStatistic (GOS) CFAR Detectors with
Automatic Censoring Technique
and Multiple Target Situations
6.10Other Adaptive CFAR Detectors
Chapter 10Clutter Map CFAR Processing
11.7Summary
Chapter 12Target Detection for High Resolution Radar
Gaussian Clutter plus Thermal Noise
and Foundation of Uniform Model
and Form Multidimensional CFAR Detection
16.1.7Expand the Amplitude Feature to Multiple Feature including Fractal Feature
16.2Problems and Suggestions

第1章绪论
参考文献
第2章经典的固定门限检测
2.1雷达目标自动检测的基本问题
2.1.1检测距离
2.1.2虚警率
2.1.3目标雷达截面积的Swerling起伏模型
2.1.4自动检测的经典问题——固定门限检测
2.2匹配滤波
2.2.1白噪声背景下的匹配滤波
2.2.2匹配滤波与相关接收
2.2.3相参脉冲串信号的匹配滤波
2.3单脉冲检测
2.3.1对非起伏目标的单脉冲线性检测
2.3.2对Swerling起伏目标的单脉冲线性检测
2.4多脉冲检测
2.4.1二元检测
2.4.2线性检测
2.4.3相参脉冲串检测
2.5小结
参考文献
第3章均值类CFAR处理方法
3.1引言
3.2基本模型描述
3.3CACFAR检测器
3.4GO和SOCFAR检测器
3.5WCACFAR检测器
3.6采用对数检波的CACFAR检测器
3.7单脉冲线性CACFAR检测器
3.8多脉冲CACFAR检测器
3.8.1双门限CACFAR检测器
3.8.2多脉冲非相参积累CACFAR检测器
3.9ML类CFAR检测器在均匀杂波背景中的性能
3.10ML类CFAR检测器在多目标环境中的性能
3.11ML类CFAR检测器在杂波边缘环境中的性能
3.12比较与总结
参考文献
 
 
第4章有序统计类CFAR处理方法
4.1引言
4.2基本模型描述
4.3OSCFAR检测器
4.4CMLDCFAR检测器
4.5TMCFAR检测器
4.6MXCMLD CFAR检测器
4.7OSGOCFAR和OSSOCFAR检测器
4.8SCFAR检测器
4.9其他OS类CFAR检测器
4.9.1CATMCFAR检测器
4.9.2SOSGOCFAR与MSCFAR检测器
4.10OS类CFAR检测器的性能分析
4.10.1在均匀杂波背景中的性能
4.10.2在多目标环境中的性能
4.10.3在杂波边缘背景中的性能
4.11比较与总结
参考文献
第5章采用自动筛选技术的GOS类CFAR检测器
5.1引言
5.2基本模型描述
5.2.1OSOS类CFAR检测器的模型描述
5.2.2OSCA类检测器的模型描述
5.2.3TMTM类检测器的模型描述
5.3GOSCA、GOSGO、GOSSOCFAR检测器
5.3.1GOSCACFAR检测器
5.3.2GOSGOCFAR检测器
5.3.3GOSSOCFAR检测器
5.4MOSCA、OSCAGO、OSCASOCFAR检测器
5.4.1MOSCACFAR检测器
5.4.2OSCAGOCFAR检测器
5.4.3OSCASOCFAR检测器
5.5MTM、TMGO、TMSOCFAR检测器
5.5.1MTMCFAR检测器
5.5.2TMGOCFAR检测器
5.5.3TMSOCFAR检测器
5.6GOS类CFAR检测器在均匀背景和多目标环境中的性能
5.6.1GOS类CFAR检测器在均匀背景中的性能
5.6.2GOS类CFAR检测器在多目标环境中的性能
5.7GOS类CFAR检测器在杂波边缘环境中的性能
5.7.1GOSCACFAR检测器在杂波边缘环境中的性能
5.7.2GOSGOCFAR和GOSSOCFAR检测器在杂波边缘环境中的性能
5.7.3MOSCACFAR检测器在杂波边缘环境中的性能
5.7.4OSCAGO,OSCASOCFAR检测器在杂波边缘环境中的性能
5.7.5MTM、TMGOCFAR检测器在杂波边缘环境中的性能
5.8比较与总结
参考文献
第6章自适应CFAR检测器
6.1引言
6.2CCACFAR检测器
6.3HCECFAR检测器
6.4ECFAR检测器
6.4.1ECFAR检测器结构
6.4.2ECFAR检测器在均匀杂波背景中的性能
6.4.3ECFAR检测器在多目标环境中的性能
6.5OSTACFAR检测器
6.5.1OSTACFAR检测器基本原理
6.5.2OSTACFAR检测器在杂波边缘环境中的性能
6.5.3OSTACFAR检测器在多目标环境中的性能
6.6VTMCFAR检测器
6.6.1VTMCFAR检测器基本原理
6.6.2VTMCFAR检测器在均匀杂波背景中的性能
6.6.3VTMCFAR检测器在多目标环境中的性能
6.6.4VTMCFAR检测器在杂波边缘环境中的性能
6.6.5VTMCFAR检测器的参数选择
6.7Himonas的一系列CFAR检测器
6.7.1GCMLDCFAR检测器
6.7.2GO/SOCFAR检测器
6.7.3ACMLDCFAR检测器
6.7.4GTLCMLDCFAR检测器
6.7.5ACGOCFAR检测器
6.8VICFAR检测器
6.8.1VICFAR检测器在不同背景中的应用
6.8.2VICFAR检测器的性能分析
6.9基于回波形状信息的删除单元平均CFAR检测器
6.9.1基于回波形状信息的删除单元平均方法
6.9.2检测性能仿真分析
6.10其他自适应CFAR检测器
6.10.1双重自适应CFAR检测器
6.10.2ACCFAR检测器
6.10.3改进的CACFAR检测器
6.10.4自适应长度CFAR检测器
6.10.5ACCAODVCFAR检测器
6.11比较与小结
参考文献
第7章经典非高斯杂波背景中的CFAR检测器
7.1引言
7.2Logt CFAR检测器
7.2.1对数正态分布中的Logt CFAR检测器
7.2.2韦布尔分布中的Logt CFAR检测器
7.3韦布尔分布中有序统计类CFAR检测器
7.3.1OSCFAR检测器在韦布尔背景中的检测性能
7.3.2OSGOCFAR检测器在韦布尔背景中的检测性能
7.3.3韦布尔背景中WeberHaykin恒虚警检测算法
7.3.4用参考单元样本的期望和中值估计c的方法
7.3.5多脉冲二进制积累下OSCFAR的检测性能
7.3.6多脉冲二进制积累下OSGOCFAR的检测性能
7.4MLHCFAR检测器
7.4.1形状参数已知时韦布尔分布背景中的MLHCFAR检测器
7.4.2形状参数未知时韦布尔分布背景中的MLHCFAR检测器
7.4.3检测概率和CFAR损失
7.5BLUECFAR检测器
7.5.1韦布尔背景中的BLUE检测器
7.5.2对数正态背景中的BLUECFAR检测器
7.6Pearson分布背景下的CFAR检测器
7.6.1Pearson分布背景下的CACFAR检测器
7.6.2Pearson分布背景下的OSCFAR检测器
7.6.3Pearson分布背景下的CMLDCFAR检测器
7.7Cauchy分布背景下的CFAR检测器
7.8比较与小结
参考文献
第8章复合高斯杂波中的CFAR处理
8.1引言
8.2复合高斯分布
8.2.1复合高斯复幅度模型
8.2.2K分布杂波包络模型
8.2.3相关K分布杂波幅度模型
8.2.4K分布杂波的仿真
8.3K分布杂波加热噪声中的检测性能
8.3.1K分布与记录数据的匹配
8.3.2杂波加噪声中目标检测的计算
8.3.3性能分析
8.4经典CFAR检测器在K分布杂波中的性能分析
8.4.1调制过程不相关的K分布杂波下CFAR检测
8.4.2调制过程完全相关的K分布杂波下CFAR检测
8.4.3调制过程部分相关时K分布杂波下CFAR检测
8.5复合高斯杂波中的CFAR检测器
8.5.1复合高斯杂波包络中的CFAR检测
8.5.2复合高斯杂波中的相参子空间CFAR检测
8.6球不变随机杂波下相参CFAR检测
8.6.1似然估计问题
8.6.2CFAR检测问题
8.6.3性能分析
8.7复合高斯杂波中的贝叶斯自适应检测器
8.7.1问题描述
8.7.2贝叶斯自适应检测器设计
8.7.3性能分析
8.8小结
参考文献
第9章非参量CFAR处理
9.1引言
9.2非参量检测器的渐近相对效率
9.3单样本非参量检测器
9.3.1符号检测器
9.3.2Wilcoxon检测器
9.4两样本非参量检测器
9.4.1广义符号检测器
9.4.2MannWhitney检测器
9.4.3Savage检测器与修正的Savage检测器
9.4.4秩方检测器与修正的秩方检测器
9.4.5几种非参量检测器的渐近相对效率
9.4.6非参量检测器采用有限样本时的检测性能
9.5次优秩非参量检测器
9.5.1局部秩检测器
9.5.2次优秩检测器
9.5.3性能分析
9.6韦布尔杂波下非参量检测器的性能分析
9.6.1韦布尔背景下量化秩非参量检测器
9.6.2韦布尔背景下广义符号非参量检测器
9.7利用逆正态得分函数修正秩的非参量检测器
9.7.1基本设计思路
9.7.2检测器设计
9.7.3性能分析
9.8比较与总结
参考文献
第10章杂波图CFAR处理
10.1引言
10.2Nitzberg杂波图技术
10.2.1Nitzberg杂波图检测的原理
10.2.2Nitzberg杂波图ADT值和虚警指标对w取值的约束
10.2.3Nitzberg杂波图在韦布尔分布中的性能
10.3杂波图单元平均CFAR平面检测技术
10.3.1基本模型描述
10.3.2均匀背景中的性能分析
10.3.3面技术与点技术的性能比较
10.4混合CM/LCFAR杂波图检测技术
10.4.1基本模型
10.4.2均匀背景中的性能分析
10.4.3存在干扰目标时的性能分析
10.5双参数杂波图检测技术
10.5.1双参数杂波图基本模型
10.5.2对目标自遮蔽的处理
10.6比较和总结
参考文献
第11章变换域CFAR处理
11.1引言
11.2频域CFAR检测
11.2.1信号和杂波噪声的离散傅里叶变换处理
11.2.2频域CACFAR检测器
11.2.3MTIFFT频域CACFAR方案
11.2.4频域奇偶处理检测器
11.3小波域CFAR检测
11.3.1基于离散小波变换的CMCFAR检测方法
11.3.2基于正交小波变换的CACFAR检测方法
11.4分数阶傅里叶变换域目标检测
11.4.1基于FRFT的LFM信号检测与参数估计
11.4.2FRFT域动目标检测器设计
11.4.3FRFT域长时间相参积累检测方法
11.5HilbertHuang变换域目标检测
11.5.1HHT基本原理
11.5.2基于IMF特性的微弱目标检测方法
11.6稀疏表示域目标检测
11.6.1信号稀疏表示模型及求解方法
11.6.2基于稀疏时频分布的雷达目标检测方法
11.6.3雷达目标检测结果与分析
11.7小结
参考文献
第12章高分辨率雷达目标检测
12.1引言
12.2距离扩展目标的信号模型
12.2.1秩1信号模型
12.2.2多秩子空间信号模型
12.3复合高斯杂波中多秩距离扩展目标的子空间检测器
12.3.1问题描述
12.3.2广义匹配子空间检测器的设计
12.3.3广义匹配子空间检测器虚警概率的计算
12.3.4广义匹配子空间检测器的自适应实现
12.3.5性能分析
12.4复合高斯杂波加热噪声中的距离扩展目标检测器
12.4.1问题描述
12.4.2热噪声的等效处理
12.4.3复合高斯杂波加热噪声中距离扩展目标检测器的设计
12.4.4检测器的性能分析
12.5SαS分布杂波中的距离扩展目标检测器
12.5.1SαS分布及PFLOM变换
12.5.2问题描述
12.5.3基于PFLOM变换的距离扩展目标检测器
12.5.4SαS分布杂波中的二元积累柯西检测器
12.6SAR图像CFAR检测研究的主要方面及杂波单元选取
12.6.1SAR图像CFAR检测研究的主要方面
12.6.2SAR图像CFAR检测的杂波单元选取
12.7基于广义Gamma杂波模型的SAR图像CFAR检测
12.7.1检测方法设计
12.7.2性能分析
12.8基于语义知识辅助的SAR图像CFAR检测
12.8.1检测方法设计
12.8.2性能分析
12.9基于密度特征的SAR图像CFAR检测快速实现
12.9.1检测方法设计
12.9.2性能分析
12.10比较与小结
参考文献
第13章多传感器分布式CFAR处理
13.1引言
13.2基于局部二元判决的分布式CFAR检测
13.2.1分布式CACFAR检测
13.2.2分布式OSCFAR检测
13.2.3分布式CFAR检测性能分析
13.3基于局部检测统计量的分布式CFAR检测
13.3.1基于R类局部检测统计量的分布式CFAR检测
13.3.2基于S类局部检测统计量的分布式CFAR检测
13.4分布式MIMO雷达CFAR检测
13.4.1目标回波经典线性模型及检测器设计
13.4.2MIMO分布孔径雷达AMF检测器性能分析
13.4.3仿真分析
13.5小结
参考文献
第14章多维CFAR处理
14.1引言
14.2阵列雷达CFAR检测
14.2.1信号模型与二元假设检验
14.2.2秩1目标模型下的阵列雷达目标检测器
14.2.3子空间目标模型下的阵列雷达目标检测器
14.2.4阵列雷达目标检测器的性质与性能
14.3基于自适应空时编码设计的二维联合CFAR检测
14.3.1信号模型及MSD检测器
14.3.2自适应空时编码设计
14.3.3仿真与分析
14.4基于空时距三维联合的自适应检测
14.4.1MIMO雷达信号模型
14.4.2匹配滤波后的空时距自适应处理
14.4.3空时距自适应处理
14.4.4算法实施与矩阵快速更新
14.4.5自适应聚焦和检测一体化处理
14.4.6仿真与分析
14.5其他多维CFAR检测
14.5.1扫描间融合CFAR检测
14.5.2极化CFAR检测
14.6小结
参考文献
第15章基于特征的CFAR处理
15.1引言
15.2海杂波时域分形特征与CFAR检测
15.2.1海尖峰判定
15.2.2海尖峰描述参数及统计特性
15.2.3海尖峰的Paretian泊松模型
15.2.4目标检测及性能分析
15.3海杂波频域分形特征与CFAR检测
15.3.1分数布朗运动在频域中的分形特性
15.3.2海杂波频谱的单一分形特性
15.3.3海杂波频谱单一分形参数的影响因素
15.3.4目标检测与性能分析
15.4海杂波时/频域多特征与目标检测
15.4.1特征提取与分析
15.4.2三维特征检测器
15.4.3检测性能分析
15.5基于深度学习的目标检测
15.5.1基于深度循环神经网络的脉压、检测一体化
15.5.2仿真与分析
15.5.3实测数据验证
15.6小结
参考文献
第16章回顾、建议与展望
16.1回顾
16.1.1形成CFAR处理理论体系
16.1.2提出GOS类CFAR检测器并建立统一模型
16.1.3延伸自适应CFAR检测
16.1.4发展多传感器分布式CFAR检测
16.1.5将CFAR处理由时域和频域拓展到多种变换域
16.1.6将CFAR处理的信息源维度由一维扩展到多维并形成多维CFAR检测
16.1.7将幅度特征拓展到分形等多种特征
16.2问题与建议
16.2.1性能分析与评价方法
16.2.2加强对目标特性的研究
16.2.3拓展CFAR研究思路
16.2.4注重新体制雷达中的CFAR处理研究
16.3研究方向展望
16.3.1多维信号CFAR处理
16.3.2背景杂波辨识与智能处理
16.3.3信号处理新方法应用与多特征CFAR处理
16.3.4其他领域的CFAR处理
参考文献
英文缩略语
 
 
 
CONTENTS Chapter 1Preface
Reference
Chapter 2Classical Detection with Fixed Threshold
2.1Fundamental Problems and Principles of Radar Automatic Detection
2.1.1Maximum Detection Range
2.1.2False Alarm Rate
2.1.3Swerlingfluctuation Models of Target Radar Cross Section
2.1.4Classical Issue of Automatic Detection—the Detection with Fixed Threshold
2.2Matched Filtering
2.2.1Matched Filtering in White Gaussian Noise Background
2.2.2Matched Filtering and Correlated Receiving
2.2.3Matched Filter for Coherent Pulsetrain Signals
2.3SinglePulse Detection
2.3.1SinglePulse Linear Detection for Nonfluctuation Target
2.3.2SinglePulse Linear Detection for Swerlingfluctuation Target
2.4MultiplePulse Detection
2.4.1Binary Detection
2.4.2Linear Detection
2.4.3Detection of Coherent PulseTrain Signals
2.5Summary
Reference
Chapter 3The CFAR Processing Methods Based on Mean Level 
3.1Introduction
3.2Description of Basic Models
3.3CACFAR Detector
3.4GO and SOCFAR Detector
3.5WCACFAR Detector
3.6CACFAR Scheme with LogarithmicLaw Detector 
3.7CACFAR Scheme with SinglePulse Linear Detector
3.8CACFAR Detector for Multiple Pulses
3.8.1CACFAR Detector with Double Threshold
3.8.2CACFAR Detector based on Multiple Pulses Noncoherent Accumulation
3.9Performance of MLCFAR Detectors in Homogeneous Background
3.10Performance of MLCFAR Detectors in Multiple Target Situations
3.11Performance of MLCFAR Detectors at Clutter Edges
3.12Comparison and Summary
Reference
Chapter 4The CFAR Processing Methods Based on Order Statistics
4.1Introduction
4.2Description of Basic Models
4.3OSCFAR Detector
4.4CMLDCFAR Detector
4.5TMCFAR Detector
4.6MXCMLD CFAR Detector
4.7OSGOCFAR and OSSOCFAR Detectors
4.8SCFAR Detector
4.9Other CFAR Detectors based on Order Statistics
4.9.1CATMCFAR Detector
4.9.2SOSGOCFAR and MSCFAR Detectors
4.10Performance of OrderStatistic CFAR Detectors
4.10.1Performance in Homogeneous Background
4.10.2Performance in Multiple Target Situations
4.10.3Performance at Clutter Edges
4.11Comparison and Summary
Reference
 
 
 
 Chapter 5The Generalized OrderStatistic (GOS) CFAR Detectors with  Automatic Censoring Technique
5.1Introduction
5.2Description of Basic Models
5.2.1Model Description of OSOS Type CFAR Detectors
5.2.2Model Description of OSCA Type CFAR Detectors
5.2.3Model Description of TMTM Type CFAR Detectors
5.3GOSCA,GOSGO,GOSSOCFAR Detectors
5.3.1GOSCACFAR Detector
5.3.2GOSGOCFAR Detector
5.3.3GOSSOCFAR Detector
5.4MOSCA,OSCAGO,OSCASOCFAR Detectors
5.4.1MOSCACFAR Detector
5.4.2OSCAGOCFAR Detector
5.4.3OSCASOCFAR Detector
5.5MTM,TMGO,TMSOCFAR Detectors
5.5.1MTMCFAR Detector
5.5.2TMGOCFAR Detector
5.5.3TMSOCFAR Detector
 
5.6Performance of GOS Type CFAR Detectors in Homogeneous Background  and Multiple Target Situations
5.6.1Performance of GOS Type CFAR Detectors in Homogeneous Background
5.6.2Performance of GOS Type CFAR Detectors in Multiple Target Situations
5.7Performance of GOS Type CFAR Detectors at Clutter Edges
5.7.1Performance of GOSCACFAR Detectors at Clutter Edges
5.7.2Performance of GOSGO,GOSSOCFAR Detectors at Clutter Edges
5.7.3Performance of MOSCACFAR Detectors at Clutter Edges
5.7.4Performance of OSCAGO,OSCASOCFAR Detectors at Clutter Edges
5.7.5Performance of MTM,TMGOCFAR Detectors at Clutter Edges
5.8Comparison and Summary
Reference
Chapter 6Adaptive CFAR Detectors
6.1Introduction
6.2CCACFAR Detector
6.3HCECFAR Detector
6.4ECFAR Detector
6.4.1ECFAR Detector Architecture
6.4.2Performance of ECFAR Detector in Homogeneous Background
6.4.3Performance of ECFAR Detector in Multiple Target Situations
6.5OSTACFAR Detector
6.5.1Principle of OSTACFAR Detector
6.5.2Performance of OSTACFAR Detector in Clutter Edge
6.5.3Performance of OSTACFAR Detector in Multiple Target Situations
6.6VTMCFAR Detector
6.6.1Principle of VTMCFAR Detector
6.6.2Performance of VTMCFAR Detector in Homogeneous Background
6.6.3Performance of VTMCFAR Detector in Multiple Target Situations
6.6.4Performance of VTMCFAR Detector in Clutter Edge
6.6.5Choice of Parameters for VTMCFAR Detector
6.7A Series of CFAR Detectors of Himonas
6.7.1GCMLDCFAR Detector
6.7.2GO/SOCFAR Detector
6.7.3ACMLDCFAR Detector
6.7.4GTLCMLDCFAR Detector
6.7.5ACGOCFAR Detector
6.8VICFAR Detector
6.8.1Application of VICFAR Detector in Different Background
6.8.2Performance Analysis of VICFAR Detector
6.9ESECA CFAR Detector
6.9.1ESECA method
6.9.2Simulation Analysis of Detection Performance
 6.10Other Adaptive CFAR Detectors
6.10.1Double Adaptive CFAR Detector
6.10.2ACCFAR Detector
6.10.3Improved CACFAR Detector
6.10.4Adaptive Length CFAR Detector
6.10.5ACCAODVCFAR Detector
6.11Comparison and Summary
Reference
Chapter 7The CFAR Detectors in Classical nonGaussian Background
7.1Introduction
7.2Logt CFAR Detector
7.2.1Logt CFAR Detector in Lognormal Distribution
7.2.2Logt CFAR Detector in Weibull Distribution
7.3OrderStatistic CFAR Detectors in Weibull Background
7.3.1Detection Performance of OSCFAR Detector in Weibull Background
7.3.2Detection Performance of OSGOCFAR Detector in Weibull Background
7.3.3WeberHaykin CFAR Scheme in Weibull Background
7.3.4Estimation of c Based on Expectation and Median of Reference Samples
7.3.5Detection Performance of OSCFAR with Binary Integration for Multiple Pulses
7.3.6Detection Performance of OSGOCFAR with Binary Integration for Multiple Pulses
7.4MLHCFAR Detector
7.4.1MLHCFAR Detector in Weibull Background with Known Shape Parameter
7.4.2MLHCFAR Detector in Weibull Background with Unknown Shape Parameter
7.4.3Detection Probability and CFAR Loss
7.5BLUECFAR Detector
7.5.1BLUE in Weibull Background
7.5.2BLUE in Lognormal Background
7.6CFAR Detectors in Pearson Distribution
7.6.1CACFAR Detectors in Pearson Distribution
7.6.2OSCFAR Detectors in Pearson Distribution
7.6.3CMLDCFAR Detectors in Pearson Distribution
7.7CFAR Detector in Cauchy Distribution
7.8Comparison and Summary
Reference
Chapter 8CFAR Processing in Compound Gaussian Clutter
8.1Introduction
8.2Compound Gaussian Distribution
8.2.1Compound Gaussian Complex Amplitude Model
8.2.2K Distributed Envelop Clutter Model
8.2.3Correlated K Distributed Clutter Model
8.2.4Simulation of K Distributed Clutter
8.3Detection Performance in K Distributed Clutter plus Thermal Noise
8.3.1Matching of K Distribution with Recorded Data
8.3.2Calculation of Detection Performance in Clutter plus Noise
8.3.3Performance Analysis
8.4Performance Analysis of Classical CFAR Detectors in K Distributed Clutter
8.4.1CFAR Detection in K Distributed Clutter with Uncorrelated Modulation Process
8.4.2CFAR Detection in K Distributed Clutter with Completely Correlated Modulation Process
8.4.3CFAR Detection in K Distributed Clutter with Partially Correlated Modulation Process
8.5Optimal CFAR Detectors in Compound Gaussian Clutter
8.5.1Optimal CFAR Detectors in Compound Gaussian Clutter Envelop
8.5.2Optimal Coherent Subspace CFAR Detectors in Compound Gaussian Clutter
8.6Coherent CFAR Detectors in Spherically Invariant Random Clutter
8.6.1Maximum Likelihood Estimation Problem
8.6.2CFAR Detection Problem
8.6.3Performance Analysis
8.7Bayesian Adaptive Detector in Compound Gaussian Clutter
8.7.1Problem Formulation
8.7.2Design of Bayesian Adaptive Detector
8.7.3Performance Analysis
8.8Summary
Reference
Chapter 9Nonparametric CFAR Detection
9.1Introduction
9.2Asymptotic Relative Efficiency for Nonparametric Detector
9.3OneSample Nonparametric Detector
9.3.1Sign Detector
9.3.2Wilcoxon Detector
9.4TwoSample Nonparametric Detector
9.4.1Generalized Sign Detector
9.4.2MannWhitney Detector
9.4.3Savage Detector and Modifier 
9.4.4Rank Squared Detector and Modifier
9.4.5Asymptotic Relative Efficiency of Several Nonparametric Detectors
9.4.6Detection Performance of Nonparametric Detector with Finite Samples
9.5Suboptimal Rank Nonparametric Detector
9.5.1Locally Optimal Rank Detector
9.5.2Suboptimal Rank Detector
9.5.3Performance Analysis
9.6Performance Analysis of Nonparametric Detectors in Weibull Clutter
9.6.1Rank Quantization Nonparameter Detector in Weibull Clutter
9.6.2Generalized Sign Nonparameter Detector in Weibull Clutter
9.7Nonparametric Detectors Using InverseNormalScore Function Modified Rank
9.7.1Basic Design Idea
9.7.2Detector Design
9.7.3Performance Analysis
9.8Comparison and Summary
Reference
 Chapter 10Clutter Map CFAR Processing
10.1Introduction
10.2Nitzbergs Clutter Map Technique
10.2.1Principle of Nitzbergs Clutter Map 
10.2.2Restriction on w by the ADT and False Alarm Rate of Nitzbergs Clutter Map
10.2.3Performance of Nitzbergs Clutter Map in Weibull Clutter
10.3Clutter Map CACFAR PlaneDetection Technique
10.3.1Basic Model Description
10.3.2Performance Analysis in Homogeneous Background
10.3.3Performance Comparison between PlaneDetection and PointDetection
10.4Hybrid CM/LCFAR Clutter Map Detection Technique
10.4.1Basic Model
10.4.2Performance in Homogeneous Background
10.4.3Performance in the Situations with Interference Target
10.5Biparametric Clutter Map Detection Technique
10.5.1Basical Model of Biparametric Clutter Map
10.5.2Target Selfmasking Avoidance
10.6Comparison and Summary
Reference
Chapter 11CFAR Processing in Transform Domain
11.1Introduction
11.2Transform Domain CFAR
11.2.1Discrete Fourier Transform of Signal,Clutter and Noise
11.2.2Frequency Domain CACFAR
11.2.3MTIFFTfrequency Domain CACFAR
11.2.4Frequency Domain Oddeven Processing Detector
11.3Wavelet domain CFAR
11.3.1CMCFAR Based on Discrete Wavelet Transform
11.3.2CACFAR Based on Orthogonal Wavelet Transform
11.4Fractional Fourier Transform Domain Target Detection
11.4.1LFM Signal Detection and Estimation via FRFT
11.4.2Moving Target Detector in FRFT Domain
11.4.3Longtime Coherent Integration in FRFT Domain
11.5HilbertHuang Transform Domain Target Detection
11.5.1Principle of HHT
11.5.2Weak Target Detection Based on IMF Property
11.6Sparse representation domain target detection
11.6.1Signal Sparse Representation Model and Solution
11.6.2Radar Target Detection Based on Sparse Timefrequency Distribution
11.6.3Radar Target Detection Result and Analysis
 11.7Summary
Reference
 Chapter 12Target Detection for High Resolution Radar
12.1Introduction
12.2Signal Model of RangeSpread Target
12.2.1Rank One Signal Model
12.2.2MultiRank Subspace Signal Model
12.3MultiRank Subspace Detector of RangeSpread Target in Compound Gaussian Clutter
12.3.1Problem Formulation
12.3.2Design of Generalized Matched Subspace Detector
12.3.3Calculation of Probability of False Alarm for Generalized Matched Subspace Detector
12.3.4Adaptive Implementation of Generalized Matched Subspace Detector
12.3.5Performance Analysis
12.4RangeSpread Target Detector in Compound Gaussian Clutter plus Thermal Noise
12.4.1Problem Formulation
12.4.2Equivalent Processing of Thermal Noise
12.4.3Design of RangeSpread Target Detector in Compound  Gaussian Clutter plus Thermal Noise
12.4.4Detection Performance Analysis
12.5Detector of RangeSpread Target in SαS Clutter
12.5.1SαS Distribution and PFLOM Transform
12.5.2Problem Formulation
12.5.3RangeSpread Target Detector based on PFLOM Transform
12.5.4Binary Integration Cauchy Detector in SαS Clutter
12.6Main Aspects of CFAR Detection for SAR Images and Selection of Clutter Cells
12.6.1Main Aspects of CFAR Detection for SAR Images
12.6.2Selection of Clutter Cells for SAR Images in CFAR Detection
12.7CFAR Detection for SAR Images based on Generalized Gamma Clutter Model
12.7.1Detector Design 
12.7.2Performance Analysis
12.8Semantic Knowledgeaided CFAR Detection for SAR Images 
12.8.1Detector Design
12.8.2Performance Analysis
12.9Fast Implementation based on Density Character of CFAR Detection for SAR Images
12.9.1Detector Design 
12.9.2Performance Analysis
12.10Comparison and Summary
Reference
Chapter 13Distributed CFAR Processing with Multisensor
13.1Introduction
13.2Distributed CFAR Detection with Multisensor based on Local Binary Decision
13.2.1Distributed CACFAR Detection
13.2.2Distributed OSCFAR Detection
13.2.3Examples for Distributed CFAR Detection
13.3Distributed CFAR Detection with Multisensor based on Local Test Statistic
13.3.1Distributed CFAR Detection based on R Type Local Test Statistic
13.3.2Distributed CFAR Detection based on S Type Local Test Statistic
13.4CFAR Detection of Distributed MIMO Radar
13.4.1the Classical Linear Model of Target Returns and Detector Design
13.4.2Performance Analysis of AMF Detector for Distributed MIMO Apertures
13.4.3Simulation and Analysis
13.5Summary
Reference
Chapter 14Multidimensional CFAR Processing
14.1Introduction
14.2CFAR Detection for Array Radar
14.2.1Signal Model and Binary Hypothesis Test 
14.2.2Array Radar Detector with Rank1 Target Model
14.2.3Array Radar Detector with Subspace Target Model
14.2.4Property and Performance of Array Radar Target Detector
14.3Twodimension CFAR Detection based on Adaptive Spacetime Coding Design
14.3.1Signal Model and MSD Detector
14.3.2Adaptive Spacetime Coding Design
14.3.3Simulation and Analysis
14.4Spacetimerange Adaptive Detection
14.4.1MIMO Radar Signal Model
14.4.2Spacetimerange Adaptive Processing after Matched Filtering
14.4.3Spacetimerange Adaptive Processing
14.4.4Implementation and Fast Matrix Update
14.4.5Adaptive Focus and Detection Integrated Processing
14.4.6Simulation and Analysis
14.5Other Multidimensional CFAR Detection
14.5.1CFAR Detection with ScantoScan Fusion
14.5.2Polarimetric CFAR Processing
14.6Summary
Reference
Chapter 15CFAR Processing Based on Feature
15.1Introduction
15.2Fractal Feature of Sea Clutter in Time Domain and CFAR Detection
15.2.1Judge of Sea Spike
15.2.2Parameter of Sea Spike and Statistics
15.2.3Paretian Possion Model of Sea Spike
15.2.4Target Detection and Performance Analysis
15.3Fractal Feature of Sea Clutter in Frequency Domain and CFAR Detection
15.3.1Fractal Property of FBM in Frequency Domain
15.3.2Monofractal Property of Sea Clutter Frequency Spectrum
15.3.3Influence Factor of Sea Clutter Monofractal Parameter
15.3.4Target Detection and Performance Analysis
15.4Multifeature of Sea Clutter in Time/Frequency Domain and Target Detection
15.4.1Feature Extraction and Analysis
15.4.2Detector Using Three Features
15.4.3Detection Performance Analysis
15.5Target Detection Based on Deep Learning
15.5.1Integration of Pulse Compression and Detection based on RNN
15.5.2Simulation and Analysis
15.5.3Verification Using Measured Data
15.6Conclusion
Reference
Chapter 16Review,Suggestion and Prospect
16.1Review
16.1.1Foundation of Theory System of CFAR Processing
16.1.2Proposal of GOS Type CFAR Detectors with Automatic Censoring Technique  and Foundation of Uniform Model
16.1.3Expand Adaptive CFAR Processing
16.1.4Develop Distributed CFAR Detection with Multisensor
16.1.5Expand CFAR Processing from Time and Frequency Domain to other Transform Domains
16.1.6Expand the Information Source Dimension of CFAR Processing from One to Many, and Form Multidimensional CFAR Detection
16.1.7Expand the Amplitude Feature to Multiple Feature including Fractal Feature
16.2Problems and Suggestions
16.2.1Performance Analysis and Evaluation Methods
16.2.2Strengthen the Research on Target Characteristics
16.2.3Expand the Research ideas about CFAR
16.2.4Pay Attention to the CFAR Processing Research in the New System Radar
16.3Prospect for Research Direction
16.3.1Multidimensional Signal CFAR Processing
16.3.2Background Clutter Identification and Intelligent Processing
16.3.3Application of New Signal Processing Method and Multifeature CFAR Processing
16.3.4CFAR Processing in Other Areas
Reference
English Abbreviation Glossary

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前　　言

Abstract
This book is a monograph about radar target detection and constant false alarm rate (CFAR) processing theory and technique.In this book,the international advances in this research field for decades and many fruits of authors research are summarized.The book is composed of sixteen chapters.The main contents are classical detection with fixed threshold,meanlevel type CFAR processing methods,orderstatistics type CFAR processing methods,generalized order statistics CFAR detectors with automatic censoring technique,adaptive CFAR detectors,classical CFAR processing in nonGaussian clutter,CFAR processing in compound Gaussian clutter,nonparametric CFAR processing,clutter map CFAR processing,CFAR processing in transform domain,detection of high resolution radar target,distributed CFAR processing with multisensor,multidimension CFAR processing and featurebased CFAR processing,the last is a brief review,suggestion and prospect.

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