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『簡體書』惯性基偏振罗盘定向信息处理技术(Information Processing Technology for Bioinspired Polarization Compass)

書城自編碼: 3971101
分類: 簡體書→大陸圖書→工業技術機械/儀表工業
作者: Donghua Zhao[赵东花]
國際書號(ISBN): 9787121474071
出版社: 电子工业出版社
出版日期: 2024-03-01

頁數/字數: /
釘裝: 平塑勒

售價:NT$ 829

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內容簡介:
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. The third part focuses on the application of cubature Kalman filter and their improvement methods in seamless combination orientation systems based on a bioinspired polarization compass. A seamless combination orientation model under discontinuous observation conditions is proposed and a discontinuous observation algorithm based on neural networks is designed.
關於作者:
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。
目錄
Chapter1 Introduction1
1.1 Development Background and Research Significance1
1.2 Bioinspired polarization orientation method3
1.3 Orientation error processing method for bioinspired polarization
compass13
1.4 Combined orientation system and method for bioinspired polarizaition
compass/inertial navigation20
Chapter2 Orientation Method and System for Atmospheric Polarization
Pattern27
2.1 Orientation method for atmospheric polarization pattern28
2.1.1 Analysis and automatic identification of neutral point characteristics of atmospheric polarization pattern28
2.1.2 Orientation algorithm based on solar meridian for imaging
bioinspired polarization compass32
2.2 Design and integration for bioinspired polarization compass based on
FPGA37
2.3 Verification of Bioinspired Polarization compass orientation test41
2.3.1 Static orientation test46
2.3.2 Turntable dynamic orientation test48
2.3.3 UAV airborne dynamic orientation test49
2.4 Chapter Summary53
Chapter3 Processing technology for Bioinspired polarization
compass noise55
3.1 Noise analysis for bioinspired polarization compass56
3.1.1 Analysis of the generation mechanism and characteristics for
polarization angle image noise56
3.1.2 Analysis of the generation mechanism and characteristics for heading
angle data noise63
3.2 Image denoising technology based on multi-scale transformation for bioinspired Polarization compass65
3.2.1 Denoising technology for polarization angle image based on
multi-scale transformation68
3.2.2 MS-PCA Image Denoising Technology based on BEMD for
Bioinspired Polarization Compass72
3.2.3 Verification of MS-PCA polarization angle image denoising method
based on BEMD77
3.3 Heading data denoising technology based on multi-scale transformation
for bioinspired polarization compass92
3.3.1 Heading data denoising technology based on multi-scale
transformation93
3.3.2 MS-TFPF heading data denoising technology based on EEMD for
bioinspired polarization compass96
3.4 Verification of heading data denoising based on multi-scale
transformation for bioinspired polarization compass104
3.5 Chapter Summary115
Chapter4 Orientation error modeling and compensation technology for
Bioinspired polarization compass118
4.1 Polarization orientation error analysis and model119
4.1.1 Analysis of polarization orientation error119
4.1.2 Model Construction for polarization orientation error125
4.2 Typical neural network models128
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 compass145
4.4 Experimental verification of orientation error model based on GRU
deep learning neural network for bioinspired polarization compass152
4.5 Chapter summary156
Chapter5 Seamless combined orientation method and system for bioinspired
polarization compass/inertial navigation158
5.1 Seamless combined orientation system for bioinspired polarization compass/inertial navigation160
5.2 Seamless combination orientation model construction for bioinspired
polarization compass/inertial navigation162

5.3 Seamless combined orientation method based on self-learning
multi-frequency residual correction for bioinspired polarization
compass/inertial navigation166
5.4 Experimental verification of the seamless combined orientation method
for bioinspired polarization compass/inertial navigation176
5.5 Chapter summary185
Chapter6 Summary and prospect187
6.1 Summary of intelligent information processing technology for
bioinspired polarization compass187
6.2 Research outlook190
References192

 

 

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