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內(nèi)容簡介

　　本書共計(jì)11章，第1章對合成孔徑雷達(dá)（SAR）目標(biāo)識別進(jìn)行了概述；第2章介紹了基于局部保持特性和混合高斯分布的SAR目標(biāo)識別；第3章介紹了基于局部保持特性和Gamma分布的SAR目標(biāo)識別；第4章介紹了基于結(jié)構(gòu)保持投影的SAR目標(biāo)識別；第5章介紹了基于類別稀疏表示的SAR目標(biāo)識別；第6章介紹了基于乘性稀疏表示和Gamma分布的SAR目標(biāo)識別；第7章介紹了基于判別統(tǒng)計(jì)字典學(xué)習(xí)的SAR目標(biāo)識別；第8章介紹了于Dempster-Shafer證據(jù)理論融合多稀疏描述和樣本統(tǒng)計(jì)特性的SAR目標(biāo)識別；第9章介紹了基于Dempster-Shafer證據(jù)理論和稀疏表示的SAR目標(biāo)識別；第10章介紹了基于兩階段稀疏結(jié)構(gòu)表示的SAR目標(biāo)識別；第11章探討了未來合成孔徑雷達(dá)目標(biāo)識別可能的發(fā)展方向。

作者簡介

　　劉明，工學(xué)博士，副教授，碩士生導(dǎo)師。2009年獲西安電子科技大學(xué)信息對抗技術(shù)專業(yè)工學(xué)學(xué)士學(xué)位，2015年獲西安電子科技大學(xué)模式識別與智能系統(tǒng)專業(yè)工學(xué)博士學(xué)位。2019年-2020年為加拿大McMaster University訪學(xué)學(xué)者。主要研究方向?yàn)椋耗繕?biāo)檢測與目標(biāo)識別。入選陜西省科協(xié)青年人才托舉計(jì)劃，獲國際無線電科學(xué)聯(lián)盟（URSI）"青年科學(xué)家”獎(jiǎng)，獲陜西省計(jì)算機(jī)學(xué)會"計(jì)算機(jī)領(lǐng)域優(yōu)秀青年專家”稱號。主持和參與了包括國家自然科學(xué)基金、國家重大基礎(chǔ)研究計(jì)劃、裝備預(yù)先研究、陜西省自然科學(xué)基金等10余項(xiàng)國家級和省部級科研項(xiàng)目。發(fā)表學(xué)術(shù)論文60余篇，授權(quán)國家發(fā)明專利10項(xiàng)（部分已轉(zhuǎn)化）。

圖書目錄

第1 章緒論························································································1
1.1 研究背景及研究意義··································································1
1.2 國內(nèi)外研究現(xiàn)狀········································································3
1.3 本書內(nèi)容介紹········································································.10
第2 章基于局部保持特性和混合高斯分布的SAR 圖像目標(biāo)識別··················.14
2.1 算法概述··············································································.14
2.2 局部保持投影算法··································································.15
2.3 基于LPP-GMD 算法的SAR 圖像目標(biāo)識別···································.16
2.3.1 基于混合高斯分布的似然函數(shù)建模····································.17
2.3.2 基于局部保持特性的先驗(yàn)函數(shù)建?！ぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁ?17
2.3.3 參數(shù)估計(jì)·····································································.18
2.4 試驗(yàn)結(jié)果與分析·····································································.22
2.5 本章小結(jié)··············································································.26
第3 章基于局部保持特性和Gamma 分布的SAR 圖像目標(biāo)識別··················.27
3.1 算法概述··············································································.27
3.2 SAR 圖像的乘性相干斑模型······················································.28
3.3 基于LPP-Gamma 算法的SAR 圖像目標(biāo)識別·································.29
3.3.1 基于Gamma 分布構(gòu)建似然函數(shù)········································.29
3.3.2 基于局部保持特性構(gòu)建先驗(yàn)函數(shù)·······································.30
3.3.3 參數(shù)估計(jì)·····································································.33
3.4 試驗(yàn)結(jié)果與分析·····································································.37
3.4.1 SAR 圖像目標(biāo)識別結(jié)果··················································.37
3.4.2 修正相似度矩陣的有效性驗(yàn)證··········································.39
3.5 本章小結(jié)··············································································.41
第4 章基于結(jié)構(gòu)保持投影的SAR 圖像目標(biāo)識別·······································.42
4.1 算法概述··············································································.42
4.2 基于CDSPP 算法的SAR 圖像目標(biāo)識別·······································.43
4.2.1 CDSPP 算法·································································.43
4.2.2 差異度矩陣分析····························································.45
4.3 試驗(yàn)結(jié)果與分析·····································································.49
4.3.1 目標(biāo)的類別識別····························································.51
4.3.2 目標(biāo)的型號識別····························································.53
4.3.3 構(gòu)建差異度矩陣的優(yōu)勢···················································.57
4.4 本章小結(jié)··············································································.59
第5 章基于類別稀疏表示的SAR 圖像目標(biāo)識別·······································.60
5.1 算法概述··············································································.60
5.2 SAR 圖像的稀疏表示模型·························································.61
5.3 SAR 圖像的類別稀疏表示模型···················································.62
5.3.1 方位角敏感特性····························································.62
5.3.2 測試樣本建模·······························································.64
5.3.3 稀疏向量求解·······························································.66
5.4 基于LSR 算法的SAR 圖像目標(biāo)識別···········································.67
5.5 試驗(yàn)結(jié)果與分析·····································································.70
5.5.1 目標(biāo)的類別識別····························································.70
5.5.2 目標(biāo)的型號識別····························································.72
5.6 本章小結(jié)··············································································.76
第6 章基于乘性稀疏表示和Gamma 分布的SAR 圖像目標(biāo)識別··················.77
6.1 算法概述··············································································.77
6.2 乘性稀疏表示算法··································································.78
6.3 試驗(yàn)結(jié)果與分析·····································································.80
6.3.1 目標(biāo)的類別識別····························································.81
6.3.2 目標(biāo)的型號識別····························································.82
6.4 本章小結(jié)··············································································.88
第7 章基于判別統(tǒng)計(jì)字典學(xué)習(xí)的SAR 圖像目標(biāo)識別·································.89
7.1