惯性基偏振罗盘定向信息处理技术

作　者：	Donghua Zhao
出版社：	电子工业出版社
丛编项：
版权说明：	本书为公共版权或经版权方授权，请支持正版图书
标　签：	暂缺

ISBN	出版时间	包装	开本	页数	字数
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作者简介

　　赵东花，博士毕业于中北大学仪器科学与技术学科，研究方向为仿生传感与智能导航。现工作于中北大学"省部共建动态测试技术”国家重点实验室，讲师，日本大阪大学电气、电子和信息工程司访问学者。主持省部级课题2项，参与国家面上项目、装备重大基础研究课题、军委科技委 173 基金、军委科技委基础加强子课题等国家/省部级项目5项。以第一作者/唯一通信作者发表高水平 SCI 论文7篇，以第一发明人授权国家发明专利3项，独立编写国家级规划教材《传感器原理与应用》（第四版）一章。

内容简介

This book systematically and comprehensively elaborates on the intelligent information processing technology for a bioinspired polarization compass. The content of this book are briefly consisted of three parts. The research background and significance of intelligent information processing technology for a bioinspired polarization compass is introduced first， which analyzes the research status， development trends， and gap with foreign countries in the field of orientation methods based on atmospheric polarization pattern， as well as the processing methods of the orientation error for a bioinspired polarization compass and integrated system information processing. Subsequently， the noise components of a bioinspired polarization compass and the impact of noise on its directional accuracy is analyzed， introducing the denoising and orientation error compensation technique based on intelligent algorithms such as multi-scale principal component analysis and multi-scale adaptive time-frequency peak filtering. Th...

图书目录

Chapter1 Introduction 1

1.1 Development Background and Research Significance 1

1.2 Bioinspired polarization orientation method 3

1.3 Orientation error processing method for bioinspired polarization

compass 13

1.4 Combined orientation system and method for bioinspired polarizaition

compass/inertial navigation 20

Chapter2 Orientation Method and System for Atmospheric Polarization

Pattern 27

2.1 Orientation method for atmospheric polarization pattern 28

2.1.1 Analysis and automatic identification of neutral point characteristics of atmospheric polarization pattern 28

2.1.2 Orientation algorithm based on solar meridian for imaging

bioinspired polarization compass 32

2.2 Design and integration for bioinspired polarization compass based on

FPGA 37

2.3 Verification of Bioinspired Polarization compass orientation test 41

2.3.1 Static orientation test 46

2.3.2 Turntable dynamic orientation test 48

2.3.3 UAV airborne dynamic orientation test 49

2.4 Chapter Summary 53

Chapter3 Processing technology for Bioinspired polarization

compass noise 55

3.1 Noise analysis for bioinspired polarization compass 56

3.1.1 Analysis of the generation mechanism and characteristics for

polarization angle image noise 56

3.1.2 Analysis of the generation mechanism and characteristics for heading

angle data noise 63

3.2 Image denoising technology based on multi-scale transformation for bioinspired Polarization compass 65

3.2.1 Denoising technology for polarization angle image based on

multi-scale transformation 68

3.2.2 MS-PCA Image Denoising Technology based on BEMD for

Bioinspired Polarization Compass 72

3.2.3 Verification of MS-PCA polarization angle image denoising method

based on BEMD 77

3.3 Heading data denoising technology based on multi-scale transformation

for bioinspired polarization compass 92

3.3.1 Heading data denoising technology based on multi-scale

transformation 93

3.3.2 MS-TFPF heading data denoising technology based on EEMD for

bioinspired polarization compass 96

3.4 Verification of heading data denoising based on multi-scale

transformation for bioinspired polarization compass 104

3.5 Chapter Summary 115

Chapter4 Orientation error modeling and compensation technology for

Bioinspired polarization compass 118

4.1 Polarization orientation error analysis and model 119

4.1.1 Analysis of polarization orientation error 119

4.1.2 Model Construction for polarization orientation error 125

4.2 Typical neural network models 128

4.2.1 Recurrent Neural Networks (RNNs) 128

4.2.2 Long Short-Term Memory Neural Networks (LSTMs) 133

4.2.3 Gated Recurrent Unit Neural Networks (GRUs) 141

4.3 Modeling and compensation of orientation error based on GRU deep

learning neural network for bioinspired polarization compass 145

4.4 Experimental verification of orientation error model based on GRU

deep learning neural network for bioinspired polarization compass 152

4.5 Chapter summary 156

Chapter5 Seamless combined orientation method and system for bioinspired

polarization compass/inertial navigation 158

5.1 Seamless combined orientation system for bioinspired polarization compass/inertial navigation 160

5.2 Seamless combination orientation model construction for bioinspired

polarization compass/inertial navigation 162

5.3 Seamless combined orientation method based on self-learning

multi-frequency residual correction for bioinspired polarization

compass/inertial navigation 166

5.4 Experimental verification of the seamless combined orientation method

for bioinspired polarization compass/inertial navigation 176

5.5 Chapter summary 185

Chapter6 Summary and prospect 187

6.1 Summary of intelligent information processing technology for

bioinspired polarization compass 187

6.2 Research outlook 190

References 192