雷達目標檢測與恒虛警處理（第3版）

第1章緒論
參考文獻
第2章經(jīng)典的固定門限檢測
2.1雷達目標自動檢測的基本問題
2.1.1最大檢測距離
2.1.2虛警率
2.1.3目標雷達截面積的Swerling起伏模型
2.1.4自動檢測的經(jīng)典問題——固定門限檢測
2.2匹配濾波
2.2.1白噪聲背景下的匹配濾波
2.2.2匹配濾波與相關(guān)接收
2.2.3相參脈沖串信號的匹配濾波
2.3單脈沖檢測
2.3.1對非起伏目標的單脈沖線性檢測
2.3.2對Swerling起伏目標的單脈沖線性檢測
2.4多脈沖檢測
2.4.1二元檢測
2.4.2線性檢測
2.4.3相參脈沖串檢測
2.5小結(jié)
參考文獻
第3章均值類CFAR處理方法
3.1引言
3.2基本模型描述
3.3CACFAR檢測器
3.4GO和SOCFAR檢測器
3.5WCACFAR檢測器
3.6采用對數(shù)檢波的CACFAR檢測器
3.7單脈沖線性CACFAR檢測器
3.8多脈沖CACFAR檢測器
3.8.1雙門限CACFAR檢測器
3.8.2多脈沖非相參積累CACFAR檢測器
3.9ML類CFAR檢測器在均勻雜波背景中的性能
3.10ML類CFAR檢測器在多目標環(huán)境中的性能
3.11ML類CFAR檢測器在雜波邊緣環(huán)境中的性能
3.12比較與總結(jié)
參考文獻
 
 
第4章有序統(tǒng)計類CFAR處理方法
4.1引言
4.2基本模型描述
4.3OSCFAR檢測器
4.4CMLDCFAR檢測器
4.5TMCFAR檢測器
4.6MXCMLD CFAR檢測器
4.7OSGOCFAR和OSSOCFAR檢測器
4.8SCFAR檢測器
4.9其他OS類CFAR檢測器
4.9.1CATMCFAR檢測器
4.9.2SOSGOCFAR與MSCFAR檢測器
4.10OS類CFAR檢測器的性能分析
4.10.1在均勻雜波背景中的性能
4.10.2在多目標環(huán)境中的性能
4.10.3在雜波邊緣背景中的性能
4.11比較與總結(jié)
參考文獻
第5章采用自動篩選技術(shù)的GOS類CFAR檢測器
5.1引言
5.2基本模型描述
5.2.1OSOS類CFAR檢測器的模型描述
5.2.2OSCA類檢測器的模型描述
5.2.3TMTM類檢測器的模型描述
5.3GOSCA、GOSGO、GOSSOCFAR檢測器
5.3.1GOSCACFAR檢測器
5.3.2GOSGOCFAR檢測器
5.3.3GOSSOCFAR檢測器
5.4MOSCA、OSCAGO、OSCASOCFAR檢測器
5.4.1MOSCACFAR檢測器
5.4.2OSCAGOCFAR檢測器
5.4.3OSCASOCFAR檢測器
5.5MTM、TMGO、TMSOCFAR檢測器
5.5.1MTMCFAR檢測器
5.5.2TMGOCFAR檢測器
5.5.3TMSOCFAR檢測器
5.6GOS類CFAR檢測器在均勻背景和多目標環(huán)境中的性能
5.6.1GOS類CFAR檢測器在均勻背景中的性能
5.6.2GOS類CFAR檢測器在多目標環(huán)境中的性能
5.7GOS類CFAR檢測器在雜波邊緣環(huán)境中的性能
5.7.1GOSCACFAR檢測器在雜波邊緣環(huán)境中的性能
5.7.2GOSGOCFAR和GOSSOCFAR檢測器在雜波邊緣環(huán)境中的性能
5.7.3MOSCACFAR檢測器在雜波邊緣環(huán)境中的性能
5.7.4OSCAGO,OSCASOCFAR檢測器在雜波邊緣環(huán)境中的性能
5.7.5MTM、TMGOCFAR檢測器在雜波邊緣環(huán)境中的性能
5.8比較與總結(jié)
參考文獻
第6章自適應(yīng)CFAR檢測器
6.1引言
6.2CCACFAR檢測器
6.3HCECFAR檢測器
6.4ECFAR檢測器
6.4.1ECFAR檢測器結(jié)構(gòu)
6.4.2ECFAR檢測器在均勻雜波背景中的性能
6.4.3ECFAR檢測器在多目標環(huán)境中的性能
6.5OSTACFAR檢測器
6.5.1OSTACFAR檢測器基本原理
6.5.2OSTACFAR檢測器在雜波邊緣環(huán)境中的性能
6.5.3OSTACFAR檢測器在多目標環(huán)境中的性能
6.6VTMCFAR檢測器
6.6.1VTMCFAR檢測器基本原理
6.6.2VTMCFAR檢測器在均勻雜波背景中的性能
6.6.3VTMCFAR檢測器在多目標環(huán)境中的性能
6.6.4VTMCFAR檢測器在雜波邊緣環(huán)境中的性能
6.6.5VTMCFAR檢測器的參數(shù)選擇
6.7Himonas的一系列CFAR檢測器
6.7.1GCMLDCFAR檢測器
6.7.2GO/SOCFAR檢測器
6.7.3ACMLDCFAR檢測器
6.7.4GTLCMLDCFAR檢測器
6.7.5ACGOCFAR檢測器
6.8VICFAR檢測器
6.8.1VICFAR檢測器在不同背景中的應(yīng)用
6.8.2VICFAR檢測器的性能分析
6.9基于回波形狀信息的刪除單元平均CFAR檢測器
6.9.1基于回波形狀信息的刪除單元平均方法
6.9.2檢測性能仿真分析
6.10其他自適應(yīng)CFAR檢測器
6.10.1雙重自適應(yīng)CFAR檢測器
6.10.2ACCFAR檢測器
6.10.3改進的CACFAR檢測器
6.10.4自適應(yīng)長度CFAR檢測器
6.10.5ACCAODVCFAR檢測器
6.11比較與小結(jié)
參考文獻
第7章經(jīng)典非高斯雜波背景中的CFAR檢測器
7.1引言
7.2Logt CFAR檢測器
7.2.1對數(shù)正態(tài)分布中的Logt CFAR檢測器
7.2.2韋布爾分布中的Logt CFAR檢測器
7.3韋布爾分布中有序統(tǒng)計類CFAR檢測器
7.3.1OSCFAR檢測器在韋布爾背景中的檢測性能
7.3.2OSGOCFAR檢測器在韋布爾背景中的檢測性能
7.3.3韋布爾背景中WeberHaykin恒虛警檢測算法
7.3.4用參考單元樣本的期望和中值估計c的方法
7.3.5多脈沖二進制積累下OSCFAR的檢測性能
7.3.6多脈沖二進制積累下OSGOCFAR的檢測性能
7.4MLHCFAR檢測器
7.4.1形狀參數(shù)已知時韋布爾分布背景中的MLHCFAR檢測器
7.4.2形狀參數(shù)未知時韋布爾分布背景中的MLHCFAR檢測器
7.4.3檢測概率和CFAR損失
7.5BLUECFAR檢測器
7.5.1韋布爾背景中的BLUE檢測器
7.5.2對數(shù)正態(tài)背景中的BLUECFAR檢測器
7.6Pearson分布背景下的CFAR檢測器
7.6.1Pearson分布背景下的CACFAR檢測器
7.6.2Pearson分布背景下的OSCFAR檢測器
7.6.3Pearson分布背景下的CMLDCFAR檢測器
7.7Cauchy分布背景下的CFAR檢測器
7.8比較與小結(jié)
參考文獻
第8章復(fù)合高斯雜波中的CFAR處理
8.1引言
8.2復(fù)合高斯分布
8.2.1復(fù)合高斯復(fù)幅度模型
8.2.2K分布雜波包絡(luò)模型
8.2.3相關(guān)K分布雜波幅度模型
8.2.4K分布雜波的仿真
8.3K分布雜波加熱噪聲中的檢測性能
8.3.1K分布與記錄數(shù)據(jù)的匹配
8.3.2雜波加噪聲中目標檢測的計算
8.3.3性能分析
8.4經(jīng)典CFAR檢測器在K分布雜波中的性能分析
8.4.1調(diào)制過程不相關(guān)的K分布雜波下CFAR檢測
8.4.2調(diào)制過程完全相關(guān)的K分布雜波下CFAR檢測
8.4.3調(diào)制過程部分相關(guān)時K分布雜波下CFAR檢測
8.5復(fù)合高斯雜波中的最優(yōu)CFAR檢測器
8.5.1復(fù)合高斯雜波包絡(luò)中的最優(yōu)CFAR檢測
8.5.2復(fù)合高斯雜波中的最優(yōu)相參子空間CFAR檢測
8.6球不變隨機雜波下相參CFAR檢測
8.6.1最大似然估計問題
8.6.2CFAR檢測問題
8.6.3性能分析
8.7復(fù)合高斯雜波中的貝葉斯自適應(yīng)檢測器
8.7.1問題描述
8.7.2貝葉斯自適應(yīng)檢測器設(shè)計
8.7.3性能分析
8.8小結(jié)
參考文獻
第9章非參量CFAR處理
9.1引言
9.2非參量檢測器的漸近相對效率
9.3單樣本非參量檢測器
9.3.1符號檢測器
9.3.2Wilcoxon檢測器
9.4兩樣本非參量檢測器
9.4.1廣義符號檢測器
9.4.2MannWhitney檢測器
9.4.3Savage檢測器與修正的Savage檢測器
9.4.4秩方檢測器與修正的秩方檢測器
9.4.5幾種非參量檢測器的漸近相對效率
9.4.6非參量檢測器采用有限樣本時的檢測性能
9.5次優(yōu)秩非參量檢測器
9.5.1局部最優(yōu)秩檢測器
9.5.2次優(yōu)秩檢測器
9.5.3性能分析
9.6韋布爾雜波下非參量檢測器的性能分析
9.6.1韋布爾背景下量化秩非參量檢測器
9.6.2韋布爾背景下廣義符號非參量檢測器
9.7利用逆正態(tài)得分函數(shù)修正秩的非參量檢測器
9.7.1基本設(shè)計思路
9.7.2檢測器設(shè)計
9.7.3性能分析
9.8比較與總結(jié)
參考文獻
第10章雜波圖CFAR處理
10.1引言
10.2Nitzberg雜波圖技術(shù)
10.2.1Nitzberg雜波圖檢測的原理
10.2.2Nitzberg雜波圖ADT值和虛警指標對w取值的約束
10.2.3Nitzberg雜波圖在韋布爾分布中的性能
10.3雜波圖單元平均CFAR平面檢測技術(shù)
10.3.1基本模型描述
10.3.2均勻背景中的性能分析
10.3.3面技術(shù)與點技術(shù)的性能比較
10.4混合CM/LCFAR雜波圖檢測技術(shù)
10.4.1基本模型
10.4.2均勻背景中的性能分析
10.4.3存在干擾目標時的性能分析
10.5雙參數(shù)雜波圖檢測技術(shù)
10.5.1雙參數(shù)雜波圖基本模型
10.5.2對目標自遮蔽的處理
10.6比較和總結(jié)
參考文獻
第11章變換域CFAR處理
11.1引言
11.2頻域CFAR檢測
11.2.1信號和雜波噪聲的離散傅里葉變換處理
11.2.2頻域CACFAR檢測器
11.2.3MTIFFT頻域CACFAR方案
11.2.4頻域奇偶處理檢測器
11.3小波域CFAR檢測
11.3.1基于離散小波變換的CMCFAR檢測方法
11.3.2基于正交小波變換的CACFAR檢測方法
11.4分數(shù)階傅里葉變換域目標檢測
11.4.1基于FRFT的LFM信號檢測與參數(shù)估計
11.4.2FRFT域動目標檢測器設(shè)計
11.4.3FRFT域長時間相參積累檢測方法
11.5HilbertHuang變換域目標檢測
11.5.1HHT基本原理
11.5.2基于IMF特性的微弱目標檢測方法
11.6稀疏表示域目標檢測
11.6.1信號稀疏表示模型及求解方法
11.6.2基于稀疏時頻分布的雷達目標檢測方法
11.6.3雷達目標檢測結(jié)果與分析
11.7小結(jié)
參考文獻
第12章高分辨率雷達目標檢測
12.1引言
12.2距離擴展目標的信號模型
12.2.1秩1信號模型
12.2.2多秩子空間信號模型
12.3復(fù)合高斯雜波中多秩距離擴展目標的子空間檢測器
12.3.1問題描述
12.3.2廣義匹配子空間檢測器的設(shè)計
12.3.3廣義匹配子空間檢測器虛警概率的計算
12.3.4廣義匹配子空間檢測器的自適應(yīng)實現(xiàn)
12.3.5性能分析
12.4復(fù)合高斯雜波加熱噪聲中的距離擴展目標檢測器
12.4.1問題描述
12.4.2熱噪聲的等效處理
12.4.3復(fù)合高斯雜波加熱噪聲中距離擴展目標檢測器的設(shè)計
12.4.4檢測器的性能分析
12.5SαS分布雜波中的距離擴展目標檢測器
12.5.1SαS分布及PFLOM變換
12.5.2問題描述
12.5.3基于PFLOM變換的距離擴展目標檢測器
12.5.4SαS分布雜波中的二元積累柯西檢測器
12.6SAR圖像CFAR檢測研究的主要方面及雜波單元選取
12.6.1SAR圖像CFAR檢測研究的主要方面
12.6.2SAR圖像CFAR檢測的雜波單元選取
12.7基于廣義Gamma雜波模型的SAR圖像CFAR檢測
12.7.1檢測方法設(shè)計
12.7.2性能分析
12.8基于語義知識輔助的SAR圖像CFAR檢測
12.8.1檢測方法設(shè)計
12.8.2性能分析
12.9基于密度特征的SAR圖像CFAR檢測快速實現(xiàn)
12.9.1檢測方法設(shè)計
12.9.2性能分析
12.10比較與小結(jié)
參考文獻
第13章多傳感器分布式CFAR處理
13.1引言
13.2基于局部二元判決的分布式CFAR檢測
13.2.1分布式CACFAR檢測
13.2.2分布式OSCFAR檢測
13.2.3分布式CFAR檢測性能分析
13.3基于局部檢測統(tǒng)計量的分布式CFAR檢測
13.3.1基于R類局部檢測統(tǒng)計量的分布式CFAR檢測
13.3.2基于S類局部檢測統(tǒng)計量的分布式CFAR檢測
13.4分布式MIMO雷達CFAR檢測
13.4.1目標回波經(jīng)典線性模型及檢測器設(shè)計
13.4.2MIMO分布孔徑雷達AMF檢測器性能分析
13.4.3仿真分析
13.5小結(jié)
參考文獻
第14章多維CFAR處理
14.1引言
14.2陣列雷達CFAR檢測
14.2.1信號模型與二元假設(shè)檢驗
14.2.2秩1目標模型下的陣列雷達目標檢測器
14.2.3子空間目標模型下的陣列雷達目標檢測器
14.2.4陣列雷達目標檢測器的性質(zhì)與性能
14.3基于自適應(yīng)空時編碼設(shè)計的二維聯(lián)合CFAR檢測
14.3.1信號模型及MSD檢測器
14.3.2自適應(yīng)空時編碼設(shè)計
14.3.3仿真與分析
14.4基于空時距三維聯(lián)合的自適應(yīng)檢測
14.4.1MIMO雷達信號模型
14.4.2匹配濾波后的空時距自適應(yīng)處理
14.4.3空時距自適應(yīng)處理
14.4.4算法實施與矩陣快速更新
14.4.5自適應(yīng)聚焦和檢測一體化處理
14.4.6仿真與分析
14.5其他多維CFAR檢測
14.5.1掃描間融合CFAR檢測
14.5.2極化CFAR檢測
14.6小結(jié)
參考文獻
第15章基于特征的CFAR處理
15.1引言
15.2海雜波時域分形特征與CFAR檢測
15.2.1海尖峰判定
15.2.2海尖峰描述參數(shù)及統(tǒng)計特性
15.2.3海尖峰的Paretian泊松模型
15.2.4目標檢測及性能分析
15.3海雜波頻域分形特征與CFAR檢測
15.3.1分數(shù)布朗運動在頻域中的分形特性
15.3.2海雜波頻譜的單一分形特性
15.3.3海雜波頻譜單一分形參數(shù)的影響因素
15.3.4目標檢測與性能分析
15.4海雜波時/頻域多特征與目標檢測
15.4.1特征提取與分析
15.4.2三維特征檢測器
15.4.3檢測性能分析
15.5基于深度學(xué)習的目標檢測
15.5.1基于深度循環(huán)神經(jīng)網(wǎng)絡(luò)的脈壓、檢測一體化
15.5.2仿真與分析
15.5.3實測數(shù)據(jù)驗證
15.6小結(jié)
參考文獻
第16章回顧、建議與展望
16.1回顧
16.1.1形成CFAR處理理論體系
16.1.2提出GOS類CFAR檢測器并建立統(tǒng)一模型
16.1.3延伸自適應(yīng)CFAR檢測
16.1.4發(fā)展多傳感器分布式CFAR檢測
16.1.5將CFAR處理由時域和頻域拓展到多種變換域
16.1.6將CFAR處理的信息源維度由一維擴展到多維并形成多維CFAR檢測
16.1.7將幅度特征拓展到分形等多種特征
16.2問題與建議
16.2.1性能分析與評價方法
16.2.2加強對目標特性的研究
16.2.3拓展CFAR研究思路
16.2.4注重新體制雷達中的CFAR處理研究
16.3研究方向展望
16.3.1多維信號CFAR處理
16.3.2背景雜波辨識與智能處理
16.3.3信號處理新方法應(yīng)用與多特征CFAR處理
16.3.4其他領(lǐng)域的CFAR處理
參考文獻
英文縮略語
 
 
 
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